Overview

Dataset statistics

Number of variables 72
Number of observations 138
Missing cells 1069
Missing cells (%) 10.8%
Duplicate rows 0
Duplicate rows (%) 0.0%
Total size in memory 77.8 KiB
Average record size in memory 576.9 B

Variable types

Categorical 60
Numeric 10
Unsupported 2

Dataset

Description Chat application experience evaluation questionnaire
Creator Matteo Busso, Massimo Stefan
Author Fausto Giunchiglia, Ivano Bison, Matteo Busso, Ronald Chenu-Abente, Marcelo Rodas Britez, Can Gunel, Giuseppe Veltri, Amalia de Götzen, Peter Kun, Amarsanaa Ganbold, Altangerel Chagnaa, George Gaskell, Miriam Bidoglia, Luca Cernuzzi, Alethia Hume, Jose Luis Zarza, Daniele Miorandi, Carlo Caprini
URL
Copyright (c) KnowDive 2022

Variable descriptions

university University
id Response ID
submitdate Date submitted
lastpage Last page
startlanguage Start language
seed Seed
token Token
startdate Date started
datestamp Date last action
ipaddr IP address
refurl Referrer URL
Q1[SQ001] First of all, the Installation Guide and implementing the ‘onboarding’ procedures for we@UNIVERSITY Please indicate how much you agree or disagree with the following two statements. [The instructions felt a bit overwhelming]
Q1[SQ002] First of all, the Installation Guide and implementing the ‘onboarding’ procedures for we@UNIVERSITY Please indicate how much you agree or disagree with the following two statements. [Setting up we@UNIVERSITY was straightforward ]
Q2[2a][1] When you asked a question, the app gave you the opportunity to select the characteristics of responders. We would like to know 1) approximately how often you used these functions 2) how useful they were.  [Selecting people with similar or different interest in the domain][Scale 1]
Q2[2a][2] When you asked a question, the app gave you the opportunity to select the characteristics of responders. We would like to know 1) approximately how often you used these functions 2) how useful they were.  [Selecting people with similar or different interest in the domain][Scale 2]
Q2[2b][1] When you asked a question, the app gave you the opportunity to select the characteristics of responders. We would like to know 1) approximately how often you used these functions 2) how useful they were.  [Selecting people with similar or different beliefs and values to you][Scale 1]
Q2[2b][2] When you asked a question, the app gave you the opportunity to select the characteristics of responders. We would like to know 1) approximately how often you used these functions 2) how useful they were.  [Selecting people with similar or different beliefs and values to you][Scale 2]
Q2[2c][1] When you asked a question, the app gave you the opportunity to select the characteristics of responders. We would like to know 1) approximately how often you used these functions 2) how useful they were.  [Whether it was a potentially sensitive question ][Scale 1]
Q2[2c][2] When you asked a question, the app gave you the opportunity to select the characteristics of responders. We would like to know 1) approximately how often you used these functions 2) how useful they were.  [Whether it was a potentially sensitive question ][Scale 2]
Q2[2d][1] When you asked a question, the app gave you the opportunity to select the characteristics of responders. We would like to know 1) approximately how often you used these functions 2) how useful they were.  [Asking questions anonymously][Scale 1]
Q2[2d][2] When you asked a question, the app gave you the opportunity to select the characteristics of responders. We would like to know 1) approximately how often you used these functions 2) how useful they were.  [Asking questions anonymously][Scale 2]
Q2[2e][1] When you asked a question, the app gave you the opportunity to select the characteristics of responders. We would like to know 1) approximately how often you used these functions 2) how useful they were.  [Selecting people who are socially close or distant from you ][Scale 1]
Q2[2e][2] When you asked a question, the app gave you the opportunity to select the characteristics of responders. We would like to know 1) approximately how often you used these functions 2) how useful they were.  [Selecting people who are socially close or distant from you ][Scale 2]
Q3[3a] And finally, do you agree or disagree with the following statement? [I understood the rationale of these filter questions]
UX02[1] Please tell us on the scale below, whether you disagree or agree with the following statements. [The chatbot was useful to reach out for help ]
UX02[2] Please tell us on the scale below, whether you disagree or agree with the following statements. [The chatbot was useful to provide help to others]
UX02[3] Please tell us on the scale below, whether you disagree or agree with the following statements. [I found the chatbot useful to get to know other students ]
UX02[4] Please tell us on the scale below, whether you disagree or agree with the following statements. [I found the chatbot useful to make me feel part of a community]
UX02[5] Please tell us on the scale below, whether you disagree or agree with the following statements. [I felt comfortable using the chatbot to ask questions]
UX02[6] Please tell us on the scale below, whether you disagree or agree with the following statements. [I felt comfortable using the chatbot to answer questions]
UX02[7] Please tell us on the scale below, whether you disagree or agree with the following statements. [I felt pleased to be able to provide an answer]
UX02[8] Please tell us on the scale below, whether you disagree or agree with the following statements. [I felt pleased to get answers to my questions]
UX02[9] Please tell us on the scale below, whether you disagree or agree with the following statements. [I found the chatbot trustworthy ]
UX02[10] Please tell us on the scale below, whether you disagree or agree with the following statements. [I would keep using the chatbot in my everyday life]
UX02[11] Please tell us on the scale below, whether you disagree or agree with the following statements. [I felt I was able to answer the questions I received]
UX02[12] Please tell us on the scale below, whether you disagree or agree with the following statements. [The short communication style (1 question-1 answer) was a bit limiting]
UX02[13] Please tell us on the scale below, whether you disagree or agree with the following statements. [I'd have liked to see others' interactions]
UX02[14] Please tell us on the scale below, whether you disagree or agree with the following statements. [I’d have liked more answers to my questions]
UX02[15] Please tell us on the scale below, whether you disagree or agree with the following statements. [I played with the settings to try to get as diverse answers as possible]
UX02[16] Please tell us on the scale below, whether you disagree or agree with the following statements. [Using chatbot in this university would benefit students ]
B01 The chatbot gave badges as people used the application.  To help us improve the badges in future versions please answer the following questions. Here is an example badge: “Congratulations! You just earned the First Question badge! Way to go!”
B03[1] Please indicate whether you agree or disagree with these statements. [I liked the chatbot's badges]
B03[2] Please indicate whether you agree or disagree with these statements. [The badges were a distraction]
B03[3] Please indicate whether you agree or disagree with these statements. [The badges encouraged me to contribute to the chatbot]
B03[4] Please indicate whether you agree or disagree with these statements. [Chatbot should be more generous with badges]
B03[5] Please indicate whether you agree or disagree with these statements. [More type of badges should be used]
B03[6] Please indicate whether you agree or disagree with these statements. [Badges based on the acceptance of answers should be used more]
B03[7] Please indicate whether you agree or disagree with these statements. [Having a badge for long answers was motivating ]
M01 In addition to badges, the chatbot sends messages on occasions. To help us improve messages in future versions please answer the following questions. Here is an example message: “You haven't asked a question yet. You can get help from the community with your questions. Type /question to ask the community!”
M03[1] Please indicate whether you agree or disagree with these statements. [I liked the chatbot’s messages]
M03[2] Please indicate whether you agree or disagree with these statements. [The messages were a distraction]
M03[3] Please indicate whether you agree or disagree with these statements. [The messages encouraged me to contribute to chatbot]
M03[4] Please indicate whether you agree or disagree with these statements. [More types of messages should be used]
M03[5] Please indicate whether you agree or disagree with these statements. [Messages should be sent less frequently]
M03[6] Please indicate whether you agree or disagree with these statements. [Messages should be personalised for each user]
M03[7] Please indicate whether you agree or disagree with these statements. [I liked the message: “Help the community with answering questions or ask a new question!"]
A[A1] Over the last year or so, how often have you done the following? [I have helped carry a stranger’s belongings]
A[A2] Over the last year or so, how often have you done the following? [I have exchanged a note for small change for a stranger]
A[A3] Over the last year or so, how often have you done the following? [I have helped an acquaintance to move houses ]
A[A4] Over the last year or so, how often have you done the following? [I have let a neighbour I did not know well borrow an item of some value to me ]
A[A5] Over the last year or so, how often have you done the following? [I have offered to help a disabled or elderly stranger across a street ]
A[A6] Over the last year or so, how often have you done the following? [I have offered my seat to a pregnant person who was standing]
A[A7] Over the last year or so, how often have you done the following? [I have spent time helping other students]
F01 Thanks for completing the questionnaire! Please click on the "Submit" button to finalise your answers. If you have any other comments on the chatbot, we would be pleased to read them.
interviewtime Total time

Alerts

submitdate has a high cardinality: 126 distinct values High cardinality
token has a high cardinality: 131 distinct values High cardinality
startdate has a high cardinality: 137 distinct values High cardinality
datestamp has a high cardinality: 137 distinct values High cardinality
ipaddr has a high cardinality: 104 distinct values High cardinality
interviewtime is highly correlated with Group time: Chatbot filters and 5 other fields High correlation
Group time: Chatbot filters is highly correlated with interviewtime High correlation
Group time: User experience is highly correlated with interviewtime and 3 other fields High correlation
Group time: Badges is highly correlated with interviewtime and 3 other fields High correlation
Group time: Messages is highly correlated with interviewtime and 3 other fields High correlation
Group time: Social behaviours is highly correlated with interviewtime and 3 other fields High correlation
Group time: Final question is highly correlated with interviewtime High correlation
interviewtime is highly correlated with Group time: Final question High correlation
Group time: Final question is highly correlated with interviewtime High correlation
interviewtime is highly correlated with Group time: User experience and 1 other fields High correlation
Group time: User experience is highly correlated with interviewtime and 2 other fields High correlation
Group time: Badges is highly correlated with interviewtime and 1 other fields High correlation
Group time: Social behaviours is highly correlated with Group time: User experience High correlation
UX02[12] is highly correlated with F01 High correlation
M03[6] is highly correlated with lastpage and 1 other fields High correlation
UX02[10] is highly correlated with F01 High correlation
UX02[3] is highly correlated with F01 High correlation
lastpage is highly correlated with M03[6] and 23 other fields High correlation
Q2[2e][1] is highly correlated with F01 High correlation
UX02[1] is highly correlated with F01 High correlation
UX02[4] is highly correlated with F01 High correlation
B03[2] is highly correlated with lastpage and 1 other fields High correlation
A[A6] is highly correlated with lastpage and 1 other fields High correlation
A[A7] is highly correlated with lastpage and 1 other fields High correlation
Q2[2c][2] is highly correlated with Q2[2d][2] and 1 other fields High correlation
UX02[14] is highly correlated with F01 High correlation
M03[1] is highly correlated with lastpage and 1 other fields High correlation
M03[5] is highly correlated with lastpage and 1 other fields High correlation
M03[3] is highly correlated with lastpage and 2 other fields High correlation
B03[4] is highly correlated with lastpage and 1 other fields High correlation
Q2[2d][2] is highly correlated with Q2[2c][2] and 1 other fields High correlation
Q1[SQ002] is highly correlated with F01 High correlation
Q2[2a][2] is highly correlated with F01 High correlation
A[A5] is highly correlated with lastpage and 1 other fields High correlation
B03[1] is highly correlated with lastpage and 2 other fields High correlation
A[A1] is highly correlated with lastpage and 1 other fields High correlation
university is highly correlated with F01 and 1 other fields High correlation
F01 is highly correlated with UX02[12] and 53 other fields High correlation
Q2[2d][1] is highly correlated with F01 and 1 other fields High correlation
Q2[2a][1] is highly correlated with F01 High correlation
UX02[8] is highly correlated with F01 High correlation
A[A2] is highly correlated with lastpage and 1 other fields High correlation
M03[7] is highly correlated with lastpage and 1 other fields High correlation
Q2[2e][2] is highly correlated with F01 High correlation
UX02[7] is highly correlated with F01 and 1 other fields High correlation
UX02[13] is highly correlated with F01 High correlation
B03[5] is highly correlated with lastpage and 2 other fields High correlation
A[A3] is highly correlated with lastpage and 2 other fields High correlation
B03[6] is highly correlated with lastpage and 2 other fields High correlation
UX02[9] is highly correlated with F01 High correlation
UX02[2] is highly correlated with F01 High correlation
B03[3] is highly correlated with lastpage and 2 other fields High correlation
Q2[2c][1] is highly correlated with F01 and 1 other fields High correlation
Q2[2b][2] is highly correlated with F01 High correlation
B03[7] is highly correlated with lastpage and 1 other fields High correlation
M03[4] is highly correlated with lastpage and 1 other fields High correlation
UX02[15] is highly correlated with F01 High correlation
Q2[2b][1] is highly correlated with F01 High correlation
UX02[11] is highly correlated with F01 High correlation
M03[2] is highly correlated with lastpage and 1 other fields High correlation
startlanguage is highly correlated with university and 1 other fields High correlation
UX02[16] is highly correlated with lastpage and 1 other fields High correlation
Q1[SQ001] is highly correlated with F01 High correlation
A[A4] is highly correlated with lastpage and 1 other fields High correlation
Q3[3a] is highly correlated with F01 High correlation
UX02[6] is highly correlated with F01 and 1 other fields High correlation
UX02[5] is highly correlated with F01 High correlation
refurl is highly correlated with lastpage and 3 other fields High correlation
university is highly correlated with id and 6 other fields High correlation
id is highly correlated with university and 2 other fields High correlation
lastpage is highly correlated with refurl and 1 other fields High correlation
startlanguage is highly correlated with university and 3 other fields High correlation
seed is highly correlated with UX02[3] and 1 other fields High correlation
refurl is highly correlated with lastpage and 12 other fields High correlation
Q1[SQ001] is highly correlated with Q1[SQ002] and 5 other fields High correlation
Q1[SQ002] is highly correlated with Q1[SQ001] and 3 other fields High correlation
Q2[2a][1] is highly correlated with Q2[2a][2] and 5 other fields High correlation
Q2[2a][2] is highly correlated with Q2[2a][1] and 3 other fields High correlation
Q2[2b][1] is highly correlated with Q2[2a][1] and 3 other fields High correlation
Q2[2b][2] is highly correlated with Q2[2a][2] and 3 other fields High correlation
Q2[2c][1] is highly correlated with Q2[2a][1] and 2 other fields High correlation
Q2[2c][2] is highly correlated with Q2[2d][2] and 1 other fields High correlation
Q2[2d][1] is highly correlated with Q2[2a][1] and 2 other fields High correlation
Q2[2d][2] is highly correlated with Q2[2c][2] and 2 other fields High correlation
Q2[2e][1] is highly correlated with Q2[2a][1] and 3 other fields High correlation
Q2[2e][2] is highly correlated with Q2[2a][2] and 3 other fields High correlation
Q3[3a] is highly correlated with UX02[2] and 1 other fields High correlation
UX02[1] is highly correlated with UX02[7] and 6 other fields High correlation
UX02[2] is highly correlated with Q3[3a] and 5 other fields High correlation
UX02[3] is highly correlated with university and 12 other fields High correlation
UX02[4] is highly correlated with UX02[3] and 13 other fields High correlation
UX02[5] is highly correlated with UX02[3] and 9 other fields High correlation
UX02[6] is highly correlated with Q1[SQ001] and 10 other fields High correlation
UX02[7] is highly correlated with Q1[SQ001] and 14 other fields High correlation
UX02[8] is highly correlated with UX02[2] and 3 other fields High correlation
UX02[9] is highly correlated with UX02[1] and 14 other fields High correlation
UX02[10] is highly correlated with refurl and 9 other fields High correlation
UX02[11] is highly correlated with university and 5 other fields High correlation
UX02[12] is highly correlated with F01 High correlation
UX02[13] is highly correlated with UX02[5] and 7 other fields High correlation
UX02[14] is highly correlated with university and 5 other fields High correlation
UX02[15] is highly correlated with refurl and 1 other fields High correlation
UX02[16] is highly correlated with lastpage and 13 other fields High correlation
B03[1] is highly correlated with UX02[1] and 14 other fields High correlation
B03[2] is highly correlated with UX02[7] and 10 other fields High correlation
B03[3] is highly correlated with UX02[1] and 11 other fields High correlation
B03[4] is highly correlated with id and 5 other fields High correlation
B03[5] is highly correlated with UX02[3] and 8 other fields High correlation
B03[6] is highly correlated with university and 11 other fields High correlation
B03[7] is highly correlated with UX02[4] and 9 other fields High correlation
M03[1] is highly correlated with Q1[SQ002] and 13 other fields High correlation
M03[2] is highly correlated with UX02[4] and 5 other fields High correlation
M03[3] is highly correlated with refurl and 3 other fields High correlation
M03[4] is highly correlated with refurl and 7 other fields High correlation
M03[5] is highly correlated with refurl and 2 other fields High correlation
M03[6] is highly correlated with UX02[13] and 2 other fields High correlation
M03[7] is highly correlated with UX02[2] and 3 other fields High correlation
A[A1] is highly correlated with A[A5] and 2 other fields High correlation
A[A2] is highly correlated with refurl and 3 other fields High correlation
A[A3] is highly correlated with refurl and 2 other fields High correlation
A[A4] is highly correlated with refurl and 3 other fields High correlation
A[A5] is highly correlated with A[A1] and 3 other fields High correlation
A[A6] is highly correlated with startlanguage and 4 other fields High correlation
A[A7] is highly correlated with A[A6] and 1 other fields High correlation
F01 is highly correlated with university and 60 other fields High correlation
interviewtime is highly correlated with refurl and 4 other fields High correlation
Group time: Onboarding procedures is highly correlated with F01 and 1 other fields High correlation
Group time: Chatbot filters is highly correlated with F01 and 2 other fields High correlation
Group time: User experience is highly correlated with refurl and 3 other fields High correlation
Group time: Badges is highly correlated with Q2[2d][2] and 4 other fields High correlation
Group time: Messages is highly correlated with Group time: Badges and 1 other fields High correlation
Group time: Social behaviours is highly correlated with refurl and 6 other fields High correlation
Group time: Final question is highly correlated with interviewtime High correlation
submitdate has 11 (8.0%) missing values Missing
lastpage has 2 (1.4%) missing values Missing
ipaddr has 25 (18.1%) missing values Missing
refurl has 122 (88.4%) missing values Missing
Q1[SQ001] has 3 (2.2%) missing values Missing
Q1[SQ002] has 3 (2.2%) missing values Missing
Q2[2a][1] has 6 (4.3%) missing values Missing
Q2[2a][2] has 8 (5.8%) missing values Missing
Q2[2b][1] has 7 (5.1%) missing values Missing
Q2[2b][2] has 8 (5.8%) missing values Missing
Q2[2c][1] has 7 (5.1%) missing values Missing
Q2[2c][2] has 8 (5.8%) missing values Missing
Q2[2d][1] has 7 (5.1%) missing values Missing
Q2[2d][2] has 8 (5.8%) missing values Missing
Q2[2e][1] has 7 (5.1%) missing values Missing
Q2[2e][2] has 8 (5.8%) missing values Missing
Q3[3a] has 6 (4.3%) missing values Missing
UX02[1] has 10 (7.2%) missing values Missing
UX02[2] has 10 (7.2%) missing values Missing
UX02[3] has 10 (7.2%) missing values Missing
UX02[4] has 10 (7.2%) missing values Missing
UX02[5] has 10 (7.2%) missing values Missing
UX02[6] has 10 (7.2%) missing values Missing
UX02[7] has 10 (7.2%) missing values Missing
UX02[8] has 10 (7.2%) missing values Missing
UX02[9] has 10 (7.2%) missing values Missing
UX02[10] has 10 (7.2%) missing values Missing
UX02[11] has 10 (7.2%) missing values Missing
UX02[12] has 10 (7.2%) missing values Missing
UX02[13] has 10 (7.2%) missing values Missing
UX02[14] has 10 (7.2%) missing values Missing
UX02[15] has 10 (7.2%) missing values Missing
UX02[16] has 10 (7.2%) missing values Missing
B01 has 138 (100.0%) missing values Missing
B03[1] has 11 (8.0%) missing values Missing
B03[2] has 11 (8.0%) missing values Missing
B03[3] has 11 (8.0%) missing values Missing
B03[4] has 11 (8.0%) missing values Missing
B03[5] has 11 (8.0%) missing values Missing
B03[6] has 11 (8.0%) missing values Missing
B03[7] has 11 (8.0%) missing values Missing
M01 has 138 (100.0%) missing values Missing
M03[1] has 11 (8.0%) missing values Missing
M03[2] has 11 (8.0%) missing values Missing
M03[3] has 11 (8.0%) missing values Missing
M03[4] has 11 (8.0%) missing values Missing
M03[5] has 11 (8.0%) missing values Missing
M03[6] has 11 (8.0%) missing values Missing
M03[7] has 11 (8.0%) missing values Missing
A[A1] has 11 (8.0%) missing values Missing
A[A2] has 11 (8.0%) missing values Missing
A[A3] has 11 (8.0%) missing values Missing
A[A4] has 11 (8.0%) missing values Missing
A[A5] has 11 (8.0%) missing values Missing
A[A6] has 11 (8.0%) missing values Missing
A[A7] has 11 (8.0%) missing values Missing
F01 has 94 (68.1%) missing values Missing
Group time: Onboarding procedures has 2 (1.4%) missing values Missing
Group time: Chatbot filters has 6 (4.3%) missing values Missing
Group time: User experience has 10 (7.2%) missing values Missing
Group time: Badges has 11 (8.0%) missing values Missing
Group time: Messages has 11 (8.0%) missing values Missing
Group time: Social behaviours has 11 (8.0%) missing values Missing
Group time: Final question has 11 (8.0%) missing values Missing
submitdate is uniformly distributed Uniform
token is uniformly distributed Uniform
startdate is uniformly distributed Uniform
datestamp is uniformly distributed Uniform
ipaddr is uniformly distributed Uniform
F01 is uniformly distributed Uniform
seed has unique values Unique
B01 is an unsupported type, check if it needs cleaning or further analysis Unsupported
M01 is an unsupported type, check if it needs cleaning or further analysis Unsupported
interviewtime has 2 (1.4%) zeros Zeros

Reproduction

Analysis started 2022-07-04 18:22:33.431317
Analysis finished 2022-07-04 18:23:34.135655
Duration 1 minute and 0.7 seconds
Software version pandas-profiling v3.2.0
Download configuration config.json

Variables

university
Categorical

HIGH CORRELATION
HIGH CORRELATION

University

Distinct 5
Distinct (%) 3.6%
Missing 0
Missing (%) 0.0%
Memory size 1.2 KiB
AAU
38
NUM
28
UNITN
27
UC
25
LSE
20

Length

Max length 5
Median length 3
Mean length 3.210144928
Min length 2

Characters and Unicode

Total characters 443
Distinct characters 10
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row NUM
2nd row NUM
3rd row NUM
4th row NUM
5th row NUM

Common Values

Value Count Frequency (%)
AAU 38
27.5%
NUM 28
20.3%
UNITN 27
19.6%
UC 25
18.1%
LSE 20
14.5%

Length

2022-07-04T20:23:34.270370 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:23:34.519667 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
aau 38
27.5%
num 28
20.3%
unitn 27
19.6%
uc 25
18.1%
lse 20
14.5%

Most occurring characters

Value Count Frequency (%)
U 118
26.6%
N 82
18.5%
A 76
17.2%
M 28
6.3%
I 27
6.1%
T 27
6.1%
C 25
5.6%
L 20
4.5%
S 20
4.5%
E 20
4.5%

Most occurring categories

Value Count Frequency (%)
Uppercase Letter 443
100.0%

Most frequent character per category

Uppercase Letter
Value Count Frequency (%)
U 118
26.6%
N 82
18.5%
A 76
17.2%
M 28
6.3%
I 27
6.1%
T 27
6.1%
C 25
5.6%
L 20
4.5%
S 20
4.5%
E 20
4.5%

Most occurring scripts

Value Count Frequency (%)
Latin 443
100.0%

Most frequent character per script

Latin
Value Count Frequency (%)
U 118
26.6%
N 82
18.5%
A 76
17.2%
M 28
6.3%
I 27
6.1%
T 27
6.1%
C 25
5.6%
L 20
4.5%
S 20
4.5%
E 20
4.5%

Most occurring blocks

Value Count Frequency (%)
ASCII 443
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
U 118
26.6%
N 82
18.5%
A 76
17.2%
M 28
6.3%
I 27
6.1%
T 27
6.1%
C 25
5.6%
L 20
4.5%
S 20
4.5%
E 20
4.5%

id
Real number (ℝ ≥0 )

HIGH CORRELATION

Response ID

Distinct 48
Distinct (%) 34.8%
Missing 0
Missing (%) 0.0%
Infinite 0
Infinite (%) 0.0%
Mean 22.64492754
Minimum 1
Maximum 48
Zeros 0
Zeros (%) 0.0%
Negative 0
Negative (%) 0.0%
Memory size 1.2 KiB
2022-07-04T20:23:34.780068 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum 1
5-th percentile 7.85
Q1 15
median 22
Q3 29
95-th percentile 41.15
Maximum 48
Range 47
Interquartile range (IQR) 14

Descriptive statistics

Standard deviation 10.14407218
Coefficient of variation (CV) 0.4479622274
Kurtosis -0.2821868632
Mean 22.64492754
Median Absolute Deviation (MAD) 7
Skewness 0.3240951041
Sum 3125
Variance 102.9022004
Monotonicity Not monotonic
2022-07-04T20:23:35.069727 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
Value Count Frequency (%)
19 5
3.6%
21 5
3.6%
16 5
3.6%
15 5
3.6%
14 5
3.6%
13 5
3.6%
12 5
3.6%
20 5
3.6%
22 5
3.6%
18 5
3.6%
Other values (38) 88
63.8%
Value Count Frequency (%)
1 1
0.7%
2 1
0.7%
3 1
0.7%
4 1
0.7%
5 1
0.7%
6 1
0.7%
7 1
0.7%
8 1
0.7%
9 1
0.7%
10 4
2.9%
Value Count Frequency (%)
48 1
0.7%
47 1
0.7%
46 1
0.7%
45 1
0.7%
44 1
0.7%
43 1
0.7%
42 1
0.7%
41 1
0.7%
40 1
0.7%
39 1
0.7%

submitdate
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Date submitted

Distinct 126
Distinct (%) 99.2%
Missing 11
Missing (%) 8.0%
Memory size 1.2 KiB
2021-12-07 14:49:00
2
2021-12-06 15:23:00
1
2022-02-07 23:43:00
1
2022-02-02 23:14:00
1
2022-02-02 17:28:00
1
Other values (121)
121

Length

Max length 19
Median length 19
Mean length 19
Min length 19

Characters and Unicode

Total characters 2413
Distinct characters 13
Distinct categories 4 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 125 ?
Unique (%) 98.4%

Sample

1st row 2022-01-27 02:37:00
2nd row 2022-01-27 09:11:00
3rd row 2022-01-27 04:42:00
4th row 2022-01-27 02:46:00
5th row 2022-01-27 02:52:00

Common Values

Value Count Frequency (%)
2021-12-07 14:49:00 2
1.4%
2021-12-06 15:23:00 1
0.7%
2022-02-07 23:43:00 1
0.7%
2022-02-02 23:14:00 1
0.7%
2022-02-02 17:28:00 1
0.7%
2022-02-01 22:27:00 1
0.7%
2022-02-01 22:14:00 1
0.7%
2022-02-01 19:33:00 1
0.7%
2022-02-01 19:02:00 1
0.7%
2022-02-01 18:15:00 1
0.7%
Other values (116) 116
84.1%
(Missing) 11
8.0%

Length

2022-07-04T20:23:35.323256 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
Value Count Frequency (%)
2022-01-27 16
6.3%
2021-12-06 16
6.3%
2021-12-07 13
5.1%
2022-01-28 13
5.1%
2021-12-20 12
4.7%
2021-12-22 7
2.8%
2021-12-21 6
2.4%
2021-12-08 5
2.0%
2022-02-01 5
2.0%
2021-12-10 4
1.6%
Other values (133) 157
61.8%

Most occurring characters

Value Count Frequency (%)
0 576
23.9%
2 530
22.0%
1 346
14.3%
- 254
10.5%
: 254
10.5%
127
5.3%
4 58
2.4%
7 56
2.3%
5 56
2.3%
3 49
2.0%
Other values (3) 107
4.4%

Most occurring categories

Value Count Frequency (%)
Decimal Number 1778
73.7%
Dash Punctuation 254
10.5%
Other Punctuation 254
10.5%
Space Separator 127
5.3%

Most frequent character per category

Decimal Number
Value Count Frequency (%)
0 576
32.4%
2 530
29.8%
1 346
19.5%
4 58
3.3%
7 56
3.1%
5 56
3.1%
3 49
2.8%
8 41
2.3%
6 39
2.2%
9 27
1.5%
Dash Punctuation
Value Count Frequency (%)
- 254
100.0%
Other Punctuation
Value Count Frequency (%)
: 254
100.0%
Space Separator
Value Count Frequency (%)
127
100.0%

Most occurring scripts

Value Count Frequency (%)
Common 2413
100.0%

Most frequent character per script

Common
Value Count Frequency (%)
0 576
23.9%
2 530
22.0%
1 346
14.3%
- 254
10.5%
: 254
10.5%
127
5.3%
4 58
2.4%
7 56
2.3%
5 56
2.3%
3 49
2.0%
Other values (3) 107
4.4%

Most occurring blocks

Value Count Frequency (%)
ASCII 2413
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
0 576
23.9%
2 530
22.0%
1 346
14.3%
- 254
10.5%
: 254
10.5%
127
5.3%
4 58
2.4%
7 56
2.3%
5 56
2.3%
3 49
2.0%
Other values (3) 107
4.4%

lastpage
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Last page

Distinct 5
Distinct (%) 3.7%
Missing 2
Missing (%) 1.4%
Memory size 1.2 KiB
7.0
127
1.0
6
2.0
1
3.0
1
-1.0
1

Length

Max length 4
Median length 3
Mean length 3.007352941
Min length 3

Characters and Unicode

Total characters 409
Distinct characters 7
Distinct categories 3 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 3 ?
Unique (%) 2.2%

Sample

1st row 7.0
2nd row 7.0
3rd row 7.0
4th row 7.0
5th row 7.0

Common Values

Value Count Frequency (%)
7.0 127
92.0%
1.0 6
4.3%
2.0 1
0.7%
3.0 1
0.7%
-1.0 1
0.7%
(Missing) 2
1.4%

Length

2022-07-04T20:23:35.542724 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:23:35.786736 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
7.0 127
93.4%
1.0 7
5.1%
2.0 1
0.7%
3.0 1
0.7%

Most occurring characters

Value Count Frequency (%)
. 136
33.3%
0 136
33.3%
7 127
31.1%
1 7
1.7%
2 1
0.2%
3 1
0.2%
- 1
0.2%

Most occurring categories

Value Count Frequency (%)
Decimal Number 272
66.5%
Other Punctuation 136
33.3%
Dash Punctuation 1
0.2%

Most frequent character per category

Decimal Number
Value Count Frequency (%)
0 136
50.0%
7 127
46.7%
1 7
2.6%
2 1
0.4%
3 1
0.4%
Other Punctuation
Value Count Frequency (%)
. 136
100.0%
Dash Punctuation
Value Count Frequency (%)
- 1
100.0%

Most occurring scripts

Value Count Frequency (%)
Common 409
100.0%

Most frequent character per script

Common
Value Count Frequency (%)
. 136
33.3%
0 136
33.3%
7 127
31.1%
1 7
1.7%
2 1
0.2%
3 1
0.2%
- 1
0.2%

Most occurring blocks

Value Count Frequency (%)
ASCII 409
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
. 136
33.3%
0 136
33.3%
7 127
31.1%
1 7
1.7%
2 1
0.2%
3 1
0.2%
- 1
0.2%

startlanguage
Categorical

HIGH CORRELATION
HIGH CORRELATION

Start language

Distinct 4
Distinct (%) 2.9%
Missing 0
Missing (%) 0.0%
Memory size 1.2 KiB
en
58
mn
28
it
27
es
25

Length

Max length 2
Median length 2
Mean length 2
Min length 2

Characters and Unicode

Total characters 276
Distinct characters 6
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row mn
2nd row mn
3rd row mn
4th row mn
5th row mn

Common Values

Value Count Frequency (%)
en 58
42.0%
mn 28
20.3%
it 27
19.6%
es 25
18.1%

Length

2022-07-04T20:23:35.998423 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:23:36.231764 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
en 58
42.0%
mn 28
20.3%
it 27
19.6%
es 25
18.1%

Most occurring characters

Value Count Frequency (%)
n 86
31.2%
e 83
30.1%
m 28
10.1%
i 27
9.8%
t 27
9.8%
s 25
9.1%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 276
100.0%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
n 86
31.2%
e 83
30.1%
m 28
10.1%
i 27
9.8%
t 27
9.8%
s 25
9.1%

Most occurring scripts

Value Count Frequency (%)
Latin 276
100.0%

Most frequent character per script

Latin
Value Count Frequency (%)
n 86
31.2%
e 83
30.1%
m 28
10.1%
i 27
9.8%
t 27
9.8%
s 25
9.1%

Most occurring blocks

Value Count Frequency (%)
ASCII 276
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
n 86
31.2%
e 83
30.1%
m 28
10.1%
i 27
9.8%
t 27
9.8%
s 25
9.1%

seed
Real number (ℝ ≥0 )

HIGH CORRELATION
UNIQUE

Seed

Distinct 138
Distinct (%) 100.0%
Missing 0
Missing (%) 0.0%
Infinite 0
Infinite (%) 0.0%
Mean 1022842819
Minimum 18468108
Maximum 2104013658
Zeros 0
Zeros (%) 0.0%
Negative 0
Negative (%) 0.0%
Memory size 1.2 KiB
2022-07-04T20:23:36.499906 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum 18468108
5-th percentile 152156679.4
Q1 622841251.8
median 1002863646
Q3 1432844476
95-th percentile 1949135638
Maximum 2104013658
Range 2085545550
Interquartile range (IQR) 810003224.8

Descriptive statistics

Standard deviation 565078793
Coefficient of variation (CV) 0.5524590706
Kurtosis -0.9539780844
Mean 1022842819
Median Absolute Deviation (MAD) 416186977
Skewness 0.1588849827
Sum 1.41152309 × 10 11
Variance 3.193140423 × 10 17
Monotonicity Not monotonic
2022-07-04T20:23:36.792573 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Value Count Frequency (%)
724927762 1
0.7%
2065484756 1
0.7%
365182285 1
0.7%
620942831 1
0.7%
914757768 1
0.7%
535991965 1
0.7%
919746357 1
0.7%
367780839 1
0.7%
1095504147 1
0.7%
1849730787 1
0.7%
Other values (128) 128
92.8%
Value Count Frequency (%)
18468108 1
0.7%
30820292 1
0.7%
37631086 1
0.7%
66484459 1
0.7%
99252106 1
0.7%
101487878 1
0.7%
128513348 1
0.7%
156329032 1
0.7%
179210870 1
0.7%
220284188 1
0.7%
Value Count Frequency (%)
2104013658 1
0.7%
2094423703 1
0.7%
2065484756 1
0.7%
2004085829 1
0.7%
2003889858 1
0.7%
1968758623 1
0.7%
1962120093 1
0.7%
1946844264 1
0.7%
1933508314 1
0.7%
1933293916 1
0.7%

token
Categorical

HIGH CARDINALITY
UNIFORM

Token

Distinct 131
Distinct (%) 94.9%
Missing 0
Missing (%) 0.0%
Memory size 1.2 KiB
gthkVYZ5DGJ8Qea
3
ixhpcAhExZ1hQl8
2
oYBO9Nzq4PfmyU5
2
1U24IijQfubDz6a
2
ZytCd8It0STpz1x
2
Other values (126)
127

Length

Max length 15
Median length 15
Mean length 15
Min length 15

Characters and Unicode

Total characters 2070
Distinct characters 62
Distinct categories 3 ?
Distinct scripts 2 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 125 ?
Unique (%) 90.6%

Sample

1st row KAEC4iQtYPDEMzQ
2nd row Vzo8xua6dLede1n
3rd row QBrZv5IrGVf5FrQ
4th row dA0kKTkinR52qXS
5th row XLaCU23r25C2z93

Common Values

Value Count Frequency (%)
gthkVYZ5DGJ8Qea 3
2.2%
ixhpcAhExZ1hQl8 2
1.4%
oYBO9Nzq4PfmyU5 2
1.4%
1U24IijQfubDz6a 2
1.4%
ZytCd8It0STpz1x 2
1.4%
6kXrGWKe2jPSTZD 2
1.4%
nCvTXyWsZ0H97DO 1
0.7%
Y6KvofYZX4ae30O 1
0.7%
au2K2RsNuv376As 1
0.7%
iUSNRDeVnORA0mW 1
0.7%
Other values (121) 121
87.7%

Length

2022-07-04T20:23:37.062779 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
Value Count Frequency (%)
gthkvyz5dgj8qea 3
2.2%
oybo9nzq4pfmyu5 2
1.4%
1u24iijqfubdz6a 2
1.4%
zytcd8it0stpz1x 2
1.4%
6kxrgwke2jpstzd 2
1.4%
ixhpcahexz1hql8 2
1.4%
ibex0xosfrazpiw 1
0.7%
ohlvysoppiq6xzg 1
0.7%
b7dwordncitrsqy 1
0.7%
xfqkvoditpcufr7 1
0.7%
Other values (121) 121
87.7%

Most occurring characters

Value Count Frequency (%)
a 67
3.2%
z 63
3.0%
1 49
2.4%
D 45
2.2%
Q 44
2.1%
h 42
2.0%
c 42
2.0%
6 41
2.0%
m 40
1.9%
A 39
1.9%
Other values (52) 1598
77.2%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 877
42.4%
Uppercase Letter 864
41.7%
Decimal Number 329
15.9%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
a 67
7.6%
z 63
7.2%
h 42
4.8%
c 42
4.8%
m 40
4.6%
i 39
4.4%
t 35
4.0%
x 34
3.9%
o 33
3.8%
n 32
3.6%
Other values (16) 450
51.3%
Uppercase Letter
Value Count Frequency (%)
D 45
5.2%
Q 44
5.1%
A 39
4.5%
Z 39
4.5%
U 38
4.4%
C 37
4.3%
Y 36
4.2%
I 36
4.2%
O 35
4.1%
P 35
4.1%
Other values (16) 480
55.6%
Decimal Number
Value Count Frequency (%)
1 49
14.9%
6 41
12.5%
2 37
11.2%
0 37
11.2%
9 36
10.9%
5 30
9.1%
8 29
8.8%
3 25
7.6%
7 23
7.0%
4 22
6.7%

Most occurring scripts

Value Count Frequency (%)
Latin 1741
84.1%
Common 329
15.9%

Most frequent character per script

Latin
Value Count Frequency (%)
a 67
3.8%
z 63
3.6%
D 45
2.6%
Q 44
2.5%
h 42
2.4%
c 42
2.4%
m 40
2.3%
A 39
2.2%
Z 39
2.2%
i 39
2.2%
Other values (42) 1281
73.6%
Common
Value Count Frequency (%)
1 49
14.9%
6 41
12.5%
2 37
11.2%
0 37
11.2%
9 36
10.9%
5 30
9.1%
8 29
8.8%
3 25
7.6%
7 23
7.0%
4 22
6.7%

Most occurring blocks

Value Count Frequency (%)
ASCII 2070
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
a 67
3.2%
z 63
3.0%
1 49
2.4%
D 45
2.2%
Q 44
2.1%
h 42
2.0%
c 42
2.0%
6 41
2.0%
m 40
1.9%
A 39
1.9%
Other values (52) 1598
77.2%

startdate
Categorical

HIGH CARDINALITY
UNIFORM

Date started

Distinct 137
Distinct (%) 99.3%
Missing 0
Missing (%) 0.0%
Memory size 1.2 KiB
2021-12-06 15:20:00
2
2022-01-27 02:33:00
1
2022-02-01 19:29:00
1
2022-01-26 18:36:00
1
2022-01-26 21:46:00
1
Other values (132)
132

Length

Max length 19
Median length 19
Mean length 19
Min length 19

Characters and Unicode

Total characters 2622
Distinct characters 13
Distinct categories 4 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 136 ?
Unique (%) 98.6%

Sample

1st row 2022-01-27 02:33:00
2nd row 2022-01-27 02:34:00
3rd row 2022-01-27 02:35:00
4th row 2022-01-27 02:42:00
5th row 2022-01-27 02:48:00

Common Values

Value Count Frequency (%)
2021-12-06 15:20:00 2
1.4%
2022-01-27 02:33:00 1
0.7%
2022-02-01 19:29:00 1
0.7%
2022-01-26 18:36:00 1
0.7%
2022-01-26 21:46:00 1
0.7%
2022-01-27 15:48:00 1
0.7%
2022-01-30 14:32:00 1
0.7%
2022-02-01 18:12:00 1
0.7%
2022-02-01 18:55:00 1
0.7%
2022-02-01 22:10:00 1
0.7%
Other values (127) 127
92.0%

Length

2022-07-04T20:23:37.570610 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
Value Count Frequency (%)
2021-12-06 18
6.5%
2022-01-27 16
5.8%
2021-12-07 15
5.4%
2021-12-20 14
5.1%
2022-01-28 13
4.7%
2021-12-22 7
2.5%
2021-12-21 7
2.5%
2021-12-08 6
2.2%
2022-02-01 5
1.8%
2022-01-18 5
1.8%
Other values (149) 170
61.6%

Most occurring characters

Value Count Frequency (%)
0 626
23.9%
2 578
22.0%
1 381
14.5%
- 276
10.5%
: 276
10.5%
138
5.3%
4 65
2.5%
3 62
2.4%
7 55
2.1%
5 50
1.9%
Other values (3) 115
4.4%

Most occurring categories

Value Count Frequency (%)
Decimal Number 1932
73.7%
Dash Punctuation 276
10.5%
Other Punctuation 276
10.5%
Space Separator 138
5.3%

Most frequent character per category

Decimal Number
Value Count Frequency (%)
0 626
32.4%
2 578
29.9%
1 381
19.7%
4 65
3.4%
3 62
3.2%
7 55
2.8%
5 50
2.6%
8 47
2.4%
6 38
2.0%
9 30
1.6%
Dash Punctuation
Value Count Frequency (%)
- 276
100.0%
Other Punctuation
Value Count Frequency (%)
: 276
100.0%
Space Separator
Value Count Frequency (%)
138
100.0%

Most occurring scripts

Value Count Frequency (%)
Common 2622
100.0%

Most frequent character per script

Common
Value Count Frequency (%)
0 626
23.9%
2 578
22.0%
1 381
14.5%
- 276
10.5%
: 276
10.5%
138
5.3%
4 65
2.5%
3 62
2.4%
7 55
2.1%
5 50
1.9%
Other values (3) 115
4.4%

Most occurring blocks

Value Count Frequency (%)
ASCII 2622
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
0 626
23.9%
2 578
22.0%
1 381
14.5%
- 276
10.5%
: 276
10.5%
138
5.3%
4 65
2.5%
3 62
2.4%
7 55
2.1%
5 50
1.9%
Other values (3) 115
4.4%

datestamp
Categorical

HIGH CARDINALITY
UNIFORM

Date last action

Distinct 137
Distinct (%) 99.3%
Missing 0
Missing (%) 0.0%
Memory size 1.2 KiB
2021-12-07 14:49:00
2
2022-02-01 19:02:00
1
2022-01-25 23:19:00
1
2022-01-26 18:41:00
1
2022-01-26 21:53:00
1
Other values (132)
132

Length

Max length 19
Median length 19
Mean length 19
Min length 19

Characters and Unicode

Total characters 2622
Distinct characters 13
Distinct categories 4 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 136 ?
Unique (%) 98.6%

Sample

1st row 2022-01-27 02:37:00
2nd row 2022-01-27 09:11:00
3rd row 2022-01-27 04:42:00
4th row 2022-01-27 02:46:00
5th row 2022-01-27 02:52:00

Common Values

Value Count Frequency (%)
2021-12-07 14:49:00 2
1.4%
2022-02-01 19:02:00 1
0.7%
2022-01-25 23:19:00 1
0.7%
2022-01-26 18:41:00 1
0.7%
2022-01-26 21:53:00 1
0.7%
2022-01-27 15:51:00 1
0.7%
2022-01-30 14:34:00 1
0.7%
2022-02-01 18:15:00 1
0.7%
2022-02-01 19:33:00 1
0.7%
2021-12-06 15:30:00 1
0.7%
Other values (127) 127
92.0%

Length

2022-07-04T20:23:37.787398 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
Value Count Frequency (%)
2021-12-06 18
6.5%
2022-01-27 16
5.8%
2021-12-07 15
5.4%
2021-12-20 14
5.1%
2022-01-28 13
4.7%
2021-12-21 7
2.5%
2021-12-22 7
2.5%
2021-12-08 6
2.2%
2022-02-01 5
1.8%
2022-01-18 5
1.8%
Other values (146) 170
61.6%

Most occurring characters

Value Count Frequency (%)
0 624
23.8%
2 573
21.9%
1 379
14.5%
- 276
10.5%
: 276
10.5%
138
5.3%
4 65
2.5%
7 59
2.3%
5 58
2.2%
3 58
2.2%
Other values (3) 116
4.4%

Most occurring categories

Value Count Frequency (%)
Decimal Number 1932
73.7%
Dash Punctuation 276
10.5%
Other Punctuation 276
10.5%
Space Separator 138
5.3%

Most frequent character per category

Decimal Number
Value Count Frequency (%)
0 624
32.3%
2 573
29.7%
1 379
19.6%
4 65
3.4%
7 59
3.1%
5 58
3.0%
3 58
3.0%
8 46
2.4%
6 41
2.1%
9 29
1.5%
Dash Punctuation
Value Count Frequency (%)
- 276
100.0%
Other Punctuation
Value Count Frequency (%)
: 276
100.0%
Space Separator
Value Count Frequency (%)
138
100.0%

Most occurring scripts

Value Count Frequency (%)
Common 2622
100.0%

Most frequent character per script

Common
Value Count Frequency (%)
0 624
23.8%
2 573
21.9%
1 379
14.5%
- 276
10.5%
: 276
10.5%
138
5.3%
4 65
2.5%
7 59
2.3%
5 58
2.2%
3 58
2.2%
Other values (3) 116
4.4%

Most occurring blocks

Value Count Frequency (%)
ASCII 2622
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
0 624
23.8%
2 573
21.9%
1 379
14.5%
- 276
10.5%
: 276
10.5%
138
5.3%
4 65
2.5%
7 59
2.3%
5 58
2.2%
3 58
2.2%
Other values (3) 116
4.4%

ipaddr
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

IP address

Distinct 104
Distinct (%) 92.0%
Missing 25
Missing (%) 18.1%
Memory size 1.2 KiB
193.205.210.46
4
185.80.118.98
2
78.134.45.210
2
87.54.50.34
2
81110247214
2
Other values (99)
101

Length

Max length 14
Median length 13
Mean length 12.55752212
Min length 10

Characters and Unicode

Total characters 1419
Distinct characters 11
Distinct categories 2 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 97 ?
Unique (%) 85.8%

Sample

1st row 202.126.89.160
2nd row 192.82.76.241
3rd row 66.181.161.4
4th row 202.126.89.46
5th row 66181188197

Common Values

Value Count Frequency (%)
193.205.210.46 4
2.9%
185.80.118.98 2
1.4%
78.134.45.210 2
1.4%
87.54.50.34 2
1.4%
81110247214 2
1.4%
130225198167 2
1.4%
188.179.88.149 2
1.4%
202.126.89.160 1
0.7%
158143251253 1
0.7%
2.104.42.75 1
0.7%
Other values (94) 94
68.1%
(Missing) 25
18.1%

Length

2022-07-04T20:23:38.029827 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
Value Count Frequency (%)
193.205.210.46 4
3.5%
78.134.45.210 2
1.8%
87.54.50.34 2
1.8%
81110247214 2
1.8%
130225198167 2
1.8%
188.179.88.149 2
1.8%
185.80.118.98 2
1.8%
202.126.89.252 1
0.9%
192.82.72.53 1
0.9%
202.21.109.47 1
0.9%
Other values (94) 94
83.2%

Most occurring characters

Value Count Frequency (%)
1 267
18.8%
. 258
18.2%
2 184
13.0%
8 113
8.0%
3 107
7.5%
9 96
6.8%
4 91
6.4%
0 89
6.3%
5 77
5.4%
6 75
5.3%

Most occurring categories

Value Count Frequency (%)
Decimal Number 1161
81.8%
Other Punctuation 258
18.2%

Most frequent character per category

Decimal Number
Value Count Frequency (%)
1 267
23.0%
2 184
15.8%
8 113
9.7%
3 107
9.2%
9 96
8.3%
4 91
7.8%
0 89
7.7%
5 77
6.6%
6 75
6.5%
7 62
5.3%
Other Punctuation
Value Count Frequency (%)
. 258
100.0%

Most occurring scripts

Value Count Frequency (%)
Common 1419
100.0%

Most frequent character per script

Common
Value Count Frequency (%)
1 267
18.8%
. 258
18.2%
2 184
13.0%
8 113
8.0%
3 107
7.5%
9 96
6.8%
4 91
6.4%
0 89
6.3%
5 77
5.4%
6 75
5.3%

Most occurring blocks

Value Count Frequency (%)
ASCII 1419
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
1 267
18.8%
. 258
18.2%
2 184
13.0%
8 113
8.0%
3 107
7.5%
9 96
6.8%
4 91
6.4%
0 89
6.3%
5 77
5.4%
6 75
5.3%

refurl
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Referrer URL

Distinct 5
Distinct (%) 31.2%
Missing 122
Missing (%) 88.4%
Memory size 1.2 KiB
android-app://com.google.android.gm/
9
https://mail.google.com/
3
https://wenet.limequery.com/admin/tokens/sa/browse/surveyid/338692
2
https://wenet.limequery.com/admin/tokens/sa/browse/surveyid/453155
1
https://wenet.limequery.com/338692
1

Length

Max length 66
Median length 36
Mean length 39.25
Min length 24

Characters and Unicode

Total characters 628
Distinct characters 34
Distinct categories 4 ?
Distinct scripts 2 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 2 ?
Unique (%) 12.5%

Sample

1st row https://wenet.limequery.com/admin/tokens/sa/browse/surveyid/453155
2nd row android-app://com.google.android.gm/
3rd row android-app://com.google.android.gm/
4th row android-app://com.google.android.gm/
5th row android-app://com.google.android.gm/

Common Values

Value Count Frequency (%)
android-app://com.google.android.gm/ 9
6.5%
https://mail.google.com/ 3
2.2%
https://wenet.limequery.com/admin/tokens/sa/browse/surveyid/338692 2
1.4%
https://wenet.limequery.com/admin/tokens/sa/browse/surveyid/453155 1
0.7%
https://wenet.limequery.com/338692 1
0.7%
(Missing) 122
88.4%

Length

2022-07-04T20:23:38.270375 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:23:38.524397 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
android-app://com.google.android.gm 9
56.2%
https://mail.google.com 3
18.8%
https://wenet.limequery.com/admin/tokens/sa/browse/surveyid/338692 2
12.5%
https://wenet.limequery.com/admin/tokens/sa/browse/surveyid/453155 1
6.2%
https://wenet.limequery.com/338692 1
6.2%

Most occurring characters

Value Count Frequency (%)
o 64
10.2%
/ 63
10.0%
d 42
6.7%
. 41
6.5%
e 37
5.9%
a 36
5.7%
m 35
5.6%
g 33
5.3%
i 31
4.9%
r 28
4.5%
Other values (24) 218
34.7%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 475
75.6%
Other Punctuation 120
19.1%
Decimal Number 24
3.8%
Dash Punctuation 9
1.4%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
o 64
13.5%
d 42
8.8%
e 37
7.8%
a 36
7.6%
m 35
7.4%
g 33
6.9%
i 31
6.5%
r 28
5.9%
n 28
5.9%
p 25
5.3%
Other values (12) 116
24.4%
Decimal Number
Value Count Frequency (%)
3 7
29.2%
6 3
12.5%
5 3
12.5%
2 3
12.5%
9 3
12.5%
8 3
12.5%
4 1
4.2%
1 1
4.2%
Other Punctuation
Value Count Frequency (%)
/ 63
52.5%
. 41
34.2%
: 16
13.3%
Dash Punctuation
Value Count Frequency (%)
- 9
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 475
75.6%
Common 153
24.4%

Most frequent character per script

Latin
Value Count Frequency (%)
o 64
13.5%
d 42
8.8%
e 37
7.8%
a 36
7.6%
m 35
7.4%
g 33
6.9%
i 31
6.5%
r 28
5.9%
n 28
5.9%
p 25
5.3%
Other values (12) 116
24.4%
Common
Value Count Frequency (%)
/ 63
41.2%
. 41
26.8%
: 16
10.5%
- 9
5.9%
3 7
4.6%
6 3
2.0%
5 3
2.0%
2 3
2.0%
9 3
2.0%
8 3
2.0%
Other values (2) 2
1.3%

Most occurring blocks

Value Count Frequency (%)
ASCII 628
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
o 64
10.2%
/ 63
10.0%
d 42
6.7%
. 41
6.5%
e 37
5.9%
a 36
5.7%
m 35
5.6%
g 33
5.3%
i 31
4.9%
r 28
4.5%
Other values (24) 218
34.7%

Q1[SQ001]
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

First of all, the Installation Guide and implementing the ‘onboarding’ procedures for we@UNIVERSITY Please indicate how much you agree or disagree with the following two statements. [The instructions felt a bit overwhelming]

Distinct 5
Distinct (%) 3.7%
Missing 3
Missing (%) 2.2%
Memory size 1.2 KiB
Disagree
60
Neither agree nor disagree
28
Agree
22
Strongly disagree
20
Strongly agree
5

Length

Max length 26
Median length 17
Mean length 12.8
Min length 5

Characters and Unicode

Total characters 1728
Distinct characters 18
Distinct categories 3 ?
Distinct scripts 2 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row Strongly disagree
2nd row Disagree
3rd row Neither agree nor disagree
4th row Agree
5th row Disagree

Common Values

Value Count Frequency (%)
Disagree 60
43.5%
Neither agree nor disagree 28
20.3%
Agree 22
15.9%
Strongly disagree 20
14.5%
Strongly agree 5
3.6%
(Missing) 3
2.2%

Length

2022-07-04T20:23:38.817256 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:23:39.066786 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
disagree 108
44.3%
agree 55
22.5%
neither 28
11.5%
nor 28
11.5%
strongly 25
10.2%

Most occurring characters

Value Count Frequency (%)
e 382
22.1%
r 244
14.1%
g 188
10.9%
a 141
8.2%
i 136
7.9%
109
6.3%
s 108
6.2%
D 60
3.5%
o 53
3.1%
t 53
3.1%
Other values (8) 254
14.7%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 1484
85.9%
Uppercase Letter 135
7.8%
Space Separator 109
6.3%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
e 382
25.7%
r 244
16.4%
g 188
12.7%
a 141
9.5%
i 136
9.2%
s 108
7.3%
o 53
3.6%
t 53
3.6%
n 53
3.6%
d 48
3.2%
Other values (3) 78
5.3%
Uppercase Letter
Value Count Frequency (%)
D 60
44.4%
N 28
20.7%
S 25
18.5%
A 22
16.3%
Space Separator
Value Count Frequency (%)
109
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 1619
93.7%
Common 109
6.3%

Most frequent character per script

Latin
Value Count Frequency (%)
e 382
23.6%
r 244
15.1%
g 188
11.6%
a 141
8.7%
i 136
8.4%
s 108
6.7%
D 60
3.7%
o 53
3.3%
t 53
3.3%
n 53
3.3%
Other values (7) 201
12.4%
Common
Value Count Frequency (%)
109
100.0%

Most occurring blocks

Value Count Frequency (%)
ASCII 1728
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
e 382
22.1%
r 244
14.1%
g 188
10.9%
a 141
8.2%
i 136
7.9%
109
6.3%
s 108
6.2%
D 60
3.5%
o 53
3.1%
t 53
3.1%
Other values (8) 254
14.7%

Q1[SQ002]
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

First of all, the Installation Guide and implementing the ‘onboarding’ procedures for we@UNIVERSITY Please indicate how much you agree or disagree with the following two statements. [Setting up we@UNIVERSITY was straightforward ]

Distinct 5
Distinct (%) 3.7%
Missing 3
Missing (%) 2.2%
Memory size 1.2 KiB
Agree
74
Neither agree nor disagree
23
Strongly agree
21
Disagree
13
Strongly disagree
4

Length

Max length 26
Median length 5
Mean length 10.62222222
Min length 5

Characters and Unicode

Total characters 1434
Distinct characters 18
Distinct categories 3 ?
Distinct scripts 2 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row Strongly agree
2nd row Agree
3rd row Agree
4th row Disagree
5th row Agree

Common Values

Value Count Frequency (%)
Agree 74
53.6%
Neither agree nor disagree 23
16.7%
Strongly agree 21
15.2%
Disagree 13
9.4%
Strongly disagree 4
2.9%
(Missing) 3
2.2%

Length

2022-07-04T20:23:39.312958 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:23:39.565911 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
agree 118
51.5%
disagree 40
17.5%
strongly 25
10.9%
neither 23
10.0%
nor 23
10.0%

Most occurring characters

Value Count Frequency (%)
e 362
25.2%
r 229
16.0%
g 183
12.8%
94
6.6%
a 84
5.9%
A 74
5.2%
i 63
4.4%
t 48
3.3%
n 48
3.3%
o 48
3.3%
Other values (8) 201
14.0%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 1205
84.0%
Uppercase Letter 135
9.4%
Space Separator 94
6.6%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
e 362
30.0%
r 229
19.0%
g 183
15.2%
a 84
7.0%
i 63
5.2%
t 48
4.0%
n 48
4.0%
o 48
4.0%
s 40
3.3%
d 27
2.2%
Other values (3) 73
6.1%
Uppercase Letter
Value Count Frequency (%)
A 74
54.8%
S 25
18.5%
N 23
17.0%
D 13
9.6%
Space Separator
Value Count Frequency (%)
94
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 1340
93.4%
Common 94
6.6%

Most frequent character per script

Latin
Value Count Frequency (%)
e 362
27.0%
r 229
17.1%
g 183
13.7%
a 84
6.3%
A 74
5.5%
i 63
4.7%
t 48
3.6%
n 48
3.6%
o 48
3.6%
s 40
3.0%
Other values (7) 161
12.0%
Common
Value Count Frequency (%)
94
100.0%

Most occurring blocks

Value Count Frequency (%)
ASCII 1434
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
e 362
25.2%
r 229
16.0%
g 183
12.8%
94
6.6%
a 84
5.9%
A 74
5.2%
i 63
4.4%
t 48
3.3%
n 48
3.3%
o 48
3.3%
Other values (8) 201
14.0%

Q2[2a][1]
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

When you asked a question, the app gave you the opportunity to select the characteristics of responders. We would like to know 1) approximately how often you used these functions 2) how useful they were.  [Selecting people with similar or different interest in the domain][Scale 1]

Distinct 3
Distinct (%) 2.3%
Missing 6
Missing (%) 4.3%
Memory size 1.2 KiB
Occasionally
66
Regularly
39
Never
27

Length

Max length 12
Median length 10.5
Mean length 9.681818182
Min length 5

Characters and Unicode

Total characters 1278
Distinct characters 16
Distinct categories 2 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row Occasionally
2nd row Occasionally
3rd row Occasionally
4th row Regularly
5th row Regularly

Common Values

Value Count Frequency (%)
Occasionally 66
47.8%
Regularly 39
28.3%
Never 27
19.6%
(Missing) 6
4.3%

Length

2022-07-04T20:23:39.822098 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:23:40.070740 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
occasionally 66
50.0%
regularly 39
29.5%
never 27
20.5%

Most occurring characters

Value Count Frequency (%)
l 210
16.4%
a 171
13.4%
c 132
10.3%
y 105
8.2%
e 93
7.3%
O 66
5.2%
s 66
5.2%
i 66
5.2%
o 66
5.2%
n 66
5.2%
Other values (6) 237
18.5%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 1146
89.7%
Uppercase Letter 132
10.3%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
l 210
18.3%
a 171
14.9%
c 132
11.5%
y 105
9.2%
e 93
8.1%
s 66
5.8%
i 66
5.8%
o 66
5.8%
n 66
5.8%
r 66
5.8%
Other values (3) 105
9.2%
Uppercase Letter
Value Count Frequency (%)
O 66
50.0%
R 39
29.5%
N 27
20.5%

Most occurring scripts

Value Count Frequency (%)
Latin 1278
100.0%

Most frequent character per script

Latin
Value Count Frequency (%)
l 210
16.4%
a 171
13.4%
c 132
10.3%
y 105
8.2%
e 93
7.3%
O 66
5.2%
s 66
5.2%
i 66
5.2%
o 66
5.2%
n 66
5.2%
Other values (6) 237
18.5%

Most occurring blocks

Value Count Frequency (%)
ASCII 1278
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
l 210
16.4%
a 171
13.4%
c 132
10.3%
y 105
8.2%
e 93
7.3%
O 66
5.2%
s 66
5.2%
i 66
5.2%
o 66
5.2%
n 66
5.2%
Other values (6) 237
18.5%

Q2[2a][2]
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

When you asked a question, the app gave you the opportunity to select the characteristics of responders. We would like to know 1) approximately how often you used these functions 2) how useful they were.  [Selecting people with similar or different interest in the domain][Scale 2]

Distinct 3
Distinct (%) 2.3%
Missing 8
Missing (%) 5.8%
Memory size 1.2 KiB
Fairly useful
80
Very useful
26
Not useful
24

Length

Max length 13
Median length 13
Mean length 12.04615385
Min length 10

Characters and Unicode

Total characters 1566
Distinct characters 15
Distinct categories 3 ?
Distinct scripts 2 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row Fairly useful
2nd row Not useful
3rd row Fairly useful
4th row Not useful
5th row Fairly useful

Common Values

Value Count Frequency (%)
Fairly useful 80
58.0%
Very useful 26
18.8%
Not useful 24
17.4%
(Missing) 8
5.8%

Length

2022-07-04T20:23:40.301415 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:23:40.561076 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
useful 130
50.0%
fairly 80
30.8%
very 26
10.0%
not 24
9.2%

Most occurring characters

Value Count Frequency (%)
u 260
16.6%
l 210
13.4%
e 156
10.0%
130
8.3%
s 130
8.3%
f 130
8.3%
r 106
6.8%
y 106
6.8%
F 80
5.1%
a 80
5.1%
Other values (5) 178
11.4%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 1306
83.4%
Space Separator 130
8.3%
Uppercase Letter 130
8.3%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
u 260
19.9%
l 210
16.1%
e 156
11.9%
s 130
10.0%
f 130
10.0%
r 106
8.1%
y 106
8.1%
a 80
6.1%
i 80
6.1%
o 24
1.8%
Uppercase Letter
Value Count Frequency (%)
F 80
61.5%
V 26
20.0%
N 24
18.5%
Space Separator
Value Count Frequency (%)
130
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 1436
91.7%
Common 130
8.3%

Most frequent character per script

Latin
Value Count Frequency (%)
u 260
18.1%
l 210
14.6%
e 156
10.9%
s 130
9.1%
f 130
9.1%
r 106
7.4%
y 106
7.4%
F 80
5.6%
a 80
5.6%
i 80
5.6%
Other values (4) 98
6.8%
Common
Value Count Frequency (%)
130
100.0%

Most occurring blocks

Value Count Frequency (%)
ASCII 1566
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
u 260
16.6%
l 210
13.4%
e 156
10.0%
130
8.3%
s 130
8.3%
f 130
8.3%
r 106
6.8%
y 106
6.8%
F 80
5.1%
a 80
5.1%
Other values (5) 178
11.4%

Q2[2b][1]
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

When you asked a question, the app gave you the opportunity to select the characteristics of responders. We would like to know 1) approximately how often you used these functions 2) how useful they were.  [Selecting people with similar or different beliefs and values to you][Scale 1]

Distinct 3
Distinct (%) 2.3%
Missing 7
Missing (%) 5.1%
Memory size 1.2 KiB
Occasionally
62
Never
46
Regularly
23

Length

Max length 12
Median length 9
Mean length 9.015267176
Min length 5

Characters and Unicode

Total characters 1181
Distinct characters 16
Distinct categories 2 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row Regularly
2nd row Never
3rd row Occasionally
4th row Regularly
5th row Regularly

Common Values

Value Count Frequency (%)
Occasionally 62
44.9%
Never 46
33.3%
Regularly 23
16.7%
(Missing) 7
5.1%

Length

2022-07-04T20:23:40.793208 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:23:41.042895 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
occasionally 62
47.3%
never 46
35.1%
regularly 23
17.6%

Most occurring characters

Value Count Frequency (%)
l 170
14.4%
a 147
12.4%
c 124
10.5%
e 115
9.7%
y 85
7.2%
r 69
5.8%
O 62
5.2%
s 62
5.2%
i 62
5.2%
o 62
5.2%
Other values (6) 223
18.9%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 1050
88.9%
Uppercase Letter 131
11.1%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
l 170
16.2%
a 147
14.0%
c 124
11.8%
e 115
11.0%
y 85
8.1%
r 69
6.6%
s 62
5.9%
i 62
5.9%
o 62
5.9%
n 62
5.9%
Other values (3) 92
8.8%
Uppercase Letter
Value Count Frequency (%)
O 62
47.3%
N 46
35.1%
R 23
17.6%

Most occurring scripts

Value Count Frequency (%)
Latin 1181
100.0%

Most frequent character per script

Latin
Value Count Frequency (%)
l 170
14.4%
a 147
12.4%
c 124
10.5%
e 115
9.7%
y 85
7.2%
r 69
5.8%
O 62
5.2%
s 62
5.2%
i 62
5.2%
o 62
5.2%
Other values (6) 223
18.9%

Most occurring blocks

Value Count Frequency (%)
ASCII 1181
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
l 170
14.4%
a 147
12.4%
c 124
10.5%
e 115
9.7%
y 85
7.2%
r 69
5.8%
O 62
5.2%
s 62
5.2%
i 62
5.2%
o 62
5.2%
Other values (6) 223
18.9%

Q2[2b][2]
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

When you asked a question, the app gave you the opportunity to select the characteristics of responders. We would like to know 1) approximately how often you used these functions 2) how useful they were.  [Selecting people with similar or different beliefs and values to you][Scale 2]

Distinct 3
Distinct (%) 2.3%
Missing 8
Missing (%) 5.8%
Memory size 1.2 KiB
Fairly useful
63
Not useful
48
Very useful
19

Length

Max length 13
Median length 11
Mean length 11.6
Min length 10

Characters and Unicode

Total characters 1508
Distinct characters 15
Distinct categories 3 ?
Distinct scripts 2 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row Very useful
2nd row Not useful
3rd row Fairly useful
4th row Not useful
5th row Very useful

Common Values

Value Count Frequency (%)
Fairly useful 63
45.7%
Not useful 48
34.8%
Very useful 19
13.8%
(Missing) 8
5.8%

Length

2022-07-04T20:23:41.268231 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:23:41.514946 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
useful 130
50.0%
fairly 63
24.2%
not 48
18.5%
very 19
7.3%

Most occurring characters

Value Count Frequency (%)
u 260
17.2%
l 193
12.8%
e 149
9.9%
130
8.6%
s 130
8.6%
f 130
8.6%
r 82
5.4%
y 82
5.4%
F 63
4.2%
a 63
4.2%
Other values (5) 226
15.0%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 1248
82.8%
Space Separator 130
8.6%
Uppercase Letter 130
8.6%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
u 260
20.8%
l 193
15.5%
e 149
11.9%
s 130
10.4%
f 130
10.4%
r 82
6.6%
y 82
6.6%
a 63
5.0%
i 63
5.0%
o 48
3.8%
Uppercase Letter
Value Count Frequency (%)
F 63
48.5%
N 48
36.9%
V 19
14.6%
Space Separator
Value Count Frequency (%)
130
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 1378
91.4%
Common 130
8.6%

Most frequent character per script

Latin
Value Count Frequency (%)
u 260
18.9%
l 193
14.0%
e 149
10.8%
s 130
9.4%
f 130
9.4%
r 82
6.0%
y 82
6.0%
F 63
4.6%
a 63
4.6%
i 63
4.6%
Other values (4) 163
11.8%
Common
Value Count Frequency (%)
130
100.0%

Most occurring blocks

Value Count Frequency (%)
ASCII 1508
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
u 260
17.2%
l 193
12.8%
e 149
9.9%
130
8.6%
s 130
8.6%
f 130
8.6%
r 82
5.4%
y 82
5.4%
F 63
4.2%
a 63
4.2%
Other values (5) 226
15.0%

Q2[2c][1]
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

When you asked a question, the app gave you the opportunity to select the characteristics of responders. We would like to know 1) approximately how often you used these functions 2) how useful they were.  [Whether it was a potentially sensitive question ][Scale 1]

Distinct 3
Distinct (%) 2.3%
Missing 7
Missing (%) 5.1%
Memory size 1.2 KiB
Occasionally
66
Never
39
Regularly
26

Length

Max length 12
Median length 12
Mean length 9.320610687
Min length 5

Characters and Unicode

Total characters 1221
Distinct characters 16
Distinct categories 2 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row Regularly
2nd row Occasionally
3rd row Regularly
4th row Regularly
5th row Regularly

Common Values

Value Count Frequency (%)
Occasionally 66
47.8%
Never 39
28.3%
Regularly 26
18.8%
(Missing) 7
5.1%

Length

2022-07-04T20:23:41.738468 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:23:41.983382 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
occasionally 66
50.4%
never 39
29.8%
regularly 26
19.8%

Most occurring characters

Value Count Frequency (%)
l 184
15.1%
a 158
12.9%
c 132
10.8%
e 104
8.5%
y 92
7.5%
O 66
5.4%
s 66
5.4%
i 66
5.4%
o 66
5.4%
n 66
5.4%
Other values (6) 221
18.1%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 1090
89.3%
Uppercase Letter 131
10.7%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
l 184
16.9%
a 158
14.5%
c 132
12.1%
e 104
9.5%
y 92
8.4%
s 66
6.1%
i 66
6.1%
o 66
6.1%
n 66
6.1%
r 65
6.0%
Other values (3) 91
8.3%
Uppercase Letter
Value Count Frequency (%)
O 66
50.4%
N 39
29.8%
R 26
19.8%

Most occurring scripts

Value Count Frequency (%)
Latin 1221
100.0%

Most frequent character per script

Latin
Value Count Frequency (%)
l 184
15.1%
a 158
12.9%
c 132
10.8%
e 104
8.5%
y 92
7.5%
O 66
5.4%
s 66
5.4%
i 66
5.4%
o 66
5.4%
n 66
5.4%
Other values (6) 221
18.1%

Most occurring blocks

Value Count Frequency (%)
ASCII 1221
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
l 184
15.1%
a 158
12.9%
c 132
10.8%
e 104
8.5%
y 92
7.5%
O 66
5.4%
s 66
5.4%
i 66
5.4%
o 66
5.4%
n 66
5.4%
Other values (6) 221
18.1%

Q2[2c][2]
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

When you asked a question, the app gave you the opportunity to select the characteristics of responders. We would like to know 1) approximately how often you used these functions 2) how useful they were.  [Whether it was a potentially sensitive question ][Scale 2]

Distinct 3
Distinct (%) 2.3%
Missing 8
Missing (%) 5.8%
Memory size 1.2 KiB
Very useful
78
Fairly useful
45
Not useful
7

Length

Max length 13
Median length 11
Mean length 11.63846154
Min length 10

Characters and Unicode

Total characters 1513
Distinct characters 15
Distinct categories 3 ?
Distinct scripts 2 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row Fairly useful
2nd row Fairly useful
3rd row Very useful
4th row Very useful
5th row Very useful

Common Values

Value Count Frequency (%)
Very useful 78
56.5%
Fairly useful 45
32.6%
Not useful 7
5.1%
(Missing) 8
5.8%

Length

2022-07-04T20:23:42.205250 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:23:42.452789 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
useful 130
50.0%
very 78
30.0%
fairly 45
17.3%
not 7
2.7%

Most occurring characters

Value Count Frequency (%)
u 260
17.2%
e 208
13.7%
l 175
11.6%
130
8.6%
s 130
8.6%
f 130
8.6%
r 123
8.1%
y 123
8.1%
V 78
5.2%
F 45
3.0%
Other values (5) 111
7.3%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 1253
82.8%
Space Separator 130
8.6%
Uppercase Letter 130
8.6%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
u 260
20.8%
e 208
16.6%
l 175
14.0%
s 130
10.4%
f 130
10.4%
r 123
9.8%
y 123
9.8%
a 45
3.6%
i 45
3.6%
o 7
0.6%
Uppercase Letter
Value Count Frequency (%)
V 78
60.0%
F 45
34.6%
N 7
5.4%
Space Separator
Value Count Frequency (%)
130
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 1383
91.4%
Common 130
8.6%

Most frequent character per script

Latin
Value Count Frequency (%)
u 260
18.8%
e 208
15.0%
l 175
12.7%
s 130
9.4%
f 130
9.4%
r 123
8.9%
y 123
8.9%
V 78
5.6%
F 45
3.3%
a 45
3.3%
Other values (4) 66
4.8%
Common
Value Count Frequency (%)
130
100.0%

Most occurring blocks

Value Count Frequency (%)
ASCII 1513
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
u 260
17.2%
e 208
13.7%
l 175
11.6%
130
8.6%
s 130
8.6%
f 130
8.6%
r 123
8.1%
y 123
8.1%
V 78
5.2%
F 45
3.0%
Other values (5) 111
7.3%

Q2[2d][1]
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

When you asked a question, the app gave you the opportunity to select the characteristics of responders. We would like to know 1) approximately how often you used these functions 2) how useful they were.  [Asking questions anonymously][Scale 1]

Distinct 3
Distinct (%) 2.3%
Missing 7
Missing (%) 5.1%
Memory size 1.2 KiB
Occasionally
71
Never
43
Regularly
17

Length

Max length 12
Median length 12
Mean length 9.312977099
Min length 5

Characters and Unicode

Total characters 1220
Distinct characters 16
Distinct categories 2 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row Occasionally
2nd row Occasionally
3rd row Occasionally
4th row Occasionally
5th row Occasionally

Common Values

Value Count Frequency (%)
Occasionally 71
51.4%
Never 43
31.2%
Regularly 17
12.3%
(Missing) 7
5.1%

Length

2022-07-04T20:23:42.676338 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:23:42.920408 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
occasionally 71
54.2%
never 43
32.8%
regularly 17
13.0%

Most occurring characters

Value Count Frequency (%)
l 176
14.4%
a 159
13.0%
c 142
11.6%
e 103
8.4%
y 88
7.2%
O 71
5.8%
s 71
5.8%
i 71
5.8%
o 71
5.8%
n 71
5.8%
Other values (6) 197
16.1%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 1089
89.3%
Uppercase Letter 131
10.7%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
l 176
16.2%
a 159
14.6%
c 142
13.0%
e 103
9.5%
y 88
8.1%
s 71
6.5%
i 71
6.5%
o 71
6.5%
n 71
6.5%
r 60
5.5%
Other values (3) 77
7.1%
Uppercase Letter
Value Count Frequency (%)
O 71
54.2%
N 43
32.8%
R 17
13.0%

Most occurring scripts

Value Count Frequency (%)
Latin 1220
100.0%

Most frequent character per script

Latin
Value Count Frequency (%)
l 176
14.4%
a 159
13.0%
c 142
11.6%
e 103
8.4%
y 88
7.2%
O 71
5.8%
s 71
5.8%
i 71
5.8%
o 71
5.8%
n 71
5.8%
Other values (6) 197
16.1%

Most occurring blocks

Value Count Frequency (%)
ASCII 1220
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
l 176
14.4%
a 159
13.0%
c 142
11.6%
e 103
8.4%
y 88
7.2%
O 71
5.8%
s 71
5.8%
i 71
5.8%
o 71
5.8%
n 71
5.8%
Other values (6) 197
16.1%

Q2[2d][2]
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

When you asked a question, the app gave you the opportunity to select the characteristics of responders. We would like to know 1) approximately how often you used these functions 2) how useful they were.  [Asking questions anonymously][Scale 2]

Distinct 3
Distinct (%) 2.3%
Missing 8
Missing (%) 5.8%
Memory size 1.2 KiB
Very useful
77
Fairly useful
45
Not useful
8

Length

Max length 13
Median length 11
Mean length 11.63076923
Min length 10

Characters and Unicode

Total characters 1512
Distinct characters 15
Distinct categories 3 ?
Distinct scripts 2 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row Very useful
2nd row Very useful
3rd row Fairly useful
4th row Very useful
5th row Very useful

Common Values

Value Count Frequency (%)
Very useful 77
55.8%
Fairly useful 45
32.6%
Not useful 8
5.8%
(Missing) 8
5.8%

Length

2022-07-04T20:23:43.142165 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:23:43.400623 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
useful 130
50.0%
very 77
29.6%
fairly 45
17.3%
not 8
3.1%

Most occurring characters

Value Count Frequency (%)
u 260
17.2%
e 207
13.7%
l 175
11.6%
130
8.6%
s 130
8.6%
f 130
8.6%
r 122
8.1%
y 122
8.1%
V 77
5.1%
F 45
3.0%
Other values (5) 114
7.5%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 1252
82.8%
Space Separator 130
8.6%
Uppercase Letter 130
8.6%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
u 260
20.8%
e 207
16.5%
l 175
14.0%
s 130
10.4%
f 130
10.4%
r 122
9.7%
y 122
9.7%
a 45
3.6%
i 45
3.6%
o 8
0.6%
Uppercase Letter
Value Count Frequency (%)
V 77
59.2%
F 45
34.6%
N 8
6.2%
Space Separator
Value Count Frequency (%)
130
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 1382
91.4%
Common 130
8.6%

Most frequent character per script

Latin
Value Count Frequency (%)
u 260
18.8%
e 207
15.0%
l 175
12.7%
s 130
9.4%
f 130
9.4%
r 122
8.8%
y 122
8.8%
V 77
5.6%
F 45
3.3%
a 45
3.3%
Other values (4) 69
5.0%
Common
Value Count Frequency (%)
130
100.0%

Most occurring blocks

Value Count Frequency (%)
ASCII 1512
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
u 260
17.2%
e 207
13.7%
l 175
11.6%
130
8.6%
s 130
8.6%
f 130
8.6%
r 122
8.1%
y 122
8.1%
V 77
5.1%
F 45
3.0%
Other values (5) 114
7.5%

Q2[2e][1]
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

When you asked a question, the app gave you the opportunity to select the characteristics of responders. We would like to know 1) approximately how often you used these functions 2) how useful they were.  [Selecting people who are socially close or distant from you ][Scale 1]

Distinct 3
Distinct (%) 2.3%
Missing 7
Missing (%) 5.1%
Memory size 1.2 KiB
Occasionally
58
Never
53
Regularly
20

Length

Max length 12
Median length 9
Mean length 8.709923664
Min length 5

Characters and Unicode

Total characters 1141
Distinct characters 16
Distinct categories 2 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row Occasionally
2nd row Occasionally
3rd row Never
4th row Regularly
5th row Occasionally

Common Values

Value Count Frequency (%)
Occasionally 58
42.0%
Never 53
38.4%
Regularly 20
14.5%
(Missing) 7
5.1%

Length

2022-07-04T20:23:43.616712 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:23:43.866306 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
occasionally 58
44.3%
never 53
40.5%
regularly 20
15.3%

Most occurring characters

Value Count Frequency (%)
l 156
13.7%
a 136
11.9%
e 126
11.0%
c 116
10.2%
y 78
6.8%
r 73
6.4%
O 58
5.1%
s 58
5.1%
i 58
5.1%
o 58
5.1%
Other values (6) 224
19.6%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 1010
88.5%
Uppercase Letter 131
11.5%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
l 156
15.4%
a 136
13.5%
e 126
12.5%
c 116
11.5%
y 78
7.7%
r 73
7.2%
s 58
5.7%
i 58
5.7%
o 58
5.7%
n 58
5.7%
Other values (3) 93
9.2%
Uppercase Letter
Value Count Frequency (%)
O 58
44.3%
N 53
40.5%
R 20
15.3%

Most occurring scripts

Value Count Frequency (%)
Latin 1141
100.0%

Most frequent character per script

Latin
Value Count Frequency (%)
l 156
13.7%
a 136
11.9%
e 126
11.0%
c 116
10.2%
y 78
6.8%
r 73
6.4%
O 58
5.1%
s 58
5.1%
i 58
5.1%
o 58
5.1%
Other values (6) 224
19.6%

Most occurring blocks

Value Count Frequency (%)
ASCII 1141
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
l 156
13.7%
a 136
11.9%
e 126
11.0%
c 116
10.2%
y 78
6.8%
r 73
6.4%
O 58
5.1%
s 58
5.1%
i 58
5.1%
o 58
5.1%
Other values (6) 224
19.6%

Q2[2e][2]
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

When you asked a question, the app gave you the opportunity to select the characteristics of responders. We would like to know 1) approximately how often you used these functions 2) how useful they were.  [Selecting people who are socially close or distant from you ][Scale 2]

Distinct 3
Distinct (%) 2.3%
Missing 8
Missing (%) 5.8%
Memory size 1.2 KiB
Fairly useful
66
Not useful
39
Very useful
25

Length

Max length 13
Median length 13
Mean length 11.71538462
Min length 10

Characters and Unicode

Total characters 1523
Distinct characters 15
Distinct categories 3 ?
Distinct scripts 2 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row Fairly useful
2nd row Not useful
3rd row Not useful
4th row Not useful
5th row Fairly useful

Common Values

Value Count Frequency (%)
Fairly useful 66
47.8%
Not useful 39
28.3%
Very useful 25
18.1%
(Missing) 8
5.8%

Length

2022-07-04T20:23:44.322464 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:23:44.568038 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
useful 130
50.0%
fairly 66
25.4%
not 39
15.0%
very 25
9.6%

Most occurring characters

Value Count Frequency (%)
u 260
17.1%
l 196
12.9%
e 155
10.2%
130
8.5%
s 130
8.5%
f 130
8.5%
r 91
6.0%
y 91
6.0%
F 66
4.3%
a 66
4.3%
Other values (5) 208
13.7%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 1263
82.9%
Space Separator 130
8.5%
Uppercase Letter 130
8.5%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
u 260
20.6%
l 196
15.5%
e 155
12.3%
s 130
10.3%
f 130
10.3%
r 91
7.2%
y 91
7.2%
a 66
5.2%
i 66
5.2%
o 39
3.1%
Uppercase Letter
Value Count Frequency (%)
F 66
50.8%
N 39
30.0%
V 25
19.2%
Space Separator
Value Count Frequency (%)
130
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 1393
91.5%
Common 130
8.5%

Most frequent character per script

Latin
Value Count Frequency (%)
u 260
18.7%
l 196
14.1%
e 155
11.1%
s 130
9.3%
f 130
9.3%
r 91
6.5%
y 91
6.5%
F 66
4.7%
a 66
4.7%
i 66
4.7%
Other values (4) 142
10.2%
Common
Value Count Frequency (%)
130
100.0%

Most occurring blocks

Value Count Frequency (%)
ASCII 1523
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
u 260
17.1%
l 196
12.9%
e 155
10.2%
130
8.5%
s 130
8.5%
f 130
8.5%
r 91
6.0%
y 91
6.0%
F 66
4.3%
a 66
4.3%
Other values (5) 208
13.7%

Q3[3a]
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

And finally, do you agree or disagree with the following statement? [I understood the rationale of these filter questions]

Distinct 4
Distinct (%) 3.0%
Missing 6
Missing (%) 4.3%
Memory size 1.2 KiB
Agree
68
Strongly agree
32
Neither agree nor disagree
17
Disagree
15

Length

Max length 26
Median length 5
Mean length 10.22727273
Min length 5

Characters and Unicode

Total characters 1350
Distinct characters 18
Distinct categories 3 ?
Distinct scripts 2 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row Strongly agree
2nd row Agree
3rd row Agree
4th row Disagree
5th row Agree

Common Values

Value Count Frequency (%)
Agree 68
49.3%
Strongly agree 32
23.2%
Neither agree nor disagree 17
12.3%
Disagree 15
10.9%
(Missing) 6
4.3%

Length

2022-07-04T20:23:44.787381 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:23:45.035210 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
agree 117
54.4%
strongly 32
14.9%
disagree 32
14.9%
neither 17
7.9%
nor 17
7.9%

Most occurring characters

Value Count Frequency (%)
e 332
24.6%
r 215
15.9%
g 181
13.4%
83
6.1%
a 81
6.0%
A 68
5.0%
t 49
3.6%
o 49
3.6%
n 49
3.6%
i 49
3.6%
Other values (8) 194
14.4%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 1135
84.1%
Uppercase Letter 132
9.8%
Space Separator 83
6.1%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
e 332
29.3%
r 215
18.9%
g 181
15.9%
a 81
7.1%
t 49
4.3%
o 49
4.3%
n 49
4.3%
i 49
4.3%
s 32
2.8%
y 32
2.8%
Other values (3) 66
5.8%
Uppercase Letter
Value Count Frequency (%)
A 68
51.5%
S 32
24.2%
N 17
12.9%
D 15
11.4%
Space Separator
Value Count Frequency (%)
83
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 1267
93.9%
Common 83
6.1%

Most frequent character per script

Latin
Value Count Frequency (%)
e 332
26.2%
r 215
17.0%
g 181
14.3%
a 81
6.4%
A 68
5.4%
t 49
3.9%
o 49
3.9%
n 49
3.9%
i 49
3.9%
s 32
2.5%
Other values (7) 162
12.8%
Common
Value Count Frequency (%)
83
100.0%

Most occurring blocks

Value Count Frequency (%)
ASCII 1350
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
e 332
24.6%
r 215
15.9%
g 181
13.4%
83
6.1%
a 81
6.0%
A 68
5.0%
t 49
3.6%
o 49
3.6%
n 49
3.6%
i 49
3.6%
Other values (8) 194
14.4%

UX02[1]
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Please tell us on the scale below, whether you disagree or agree with the following statements. [The chatbot was useful to reach out for help ]

Distinct 5
Distinct (%) 3.9%
Missing 10
Missing (%) 7.2%
Memory size 1.2 KiB
Agree
71
Neither agree nor disagree
25
Strongly agree
23
Disagree
8
Strongly disagree
1

Length

Max length 26
Median length 5
Mean length 11
Min length 5

Characters and Unicode

Total characters 1408
Distinct characters 18
Distinct categories 3 ?
Distinct scripts 2 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 1 ?
Unique (%) 0.8%

Sample

1st row Agree
2nd row Neither agree nor disagree
3rd row Agree
4th row Agree
5th row Strongly agree

Common Values

Value Count Frequency (%)
Agree 71
51.4%
Neither agree nor disagree 25
18.1%
Strongly agree 23
16.7%
Disagree 8
5.8%
Strongly disagree 1
0.7%
(Missing) 10
7.2%

Length

2022-07-04T20:23:45.277701 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:23:45.533353 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
agree 119
52.4%
disagree 34
15.0%
neither 25
11.0%
nor 25
11.0%
strongly 24
10.6%

Most occurring characters

Value Count Frequency (%)
e 356
25.3%
r 227
16.1%
g 177
12.6%
99
7.0%
a 82
5.8%
A 71
5.0%
i 59
4.2%
t 49
3.5%
n 49
3.5%
o 49
3.5%
Other values (8) 190
13.5%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 1181
83.9%
Uppercase Letter 128
9.1%
Space Separator 99
7.0%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
e 356
30.1%
r 227
19.2%
g 177
15.0%
a 82
6.9%
i 59
5.0%
t 49
4.1%
n 49
4.1%
o 49
4.1%
s 34
2.9%
d 26
2.2%
Other values (3) 73
6.2%
Uppercase Letter
Value Count Frequency (%)
A 71
55.5%
N 25
19.5%
S 24
18.8%
D 8
6.2%
Space Separator
Value Count Frequency (%)
99
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 1309
93.0%
Common 99
7.0%

Most frequent character per script

Latin
Value Count Frequency (%)
e 356
27.2%
r 227
17.3%
g 177
13.5%
a 82
6.3%
A 71
5.4%
i 59
4.5%
t 49
3.7%
n 49
3.7%
o 49
3.7%
s 34
2.6%
Other values (7) 156
11.9%
Common
Value Count Frequency (%)
99
100.0%

Most occurring blocks

Value Count Frequency (%)
ASCII 1408
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
e 356
25.3%
r 227
16.1%
g 177
12.6%
99
7.0%
a 82
5.8%
A 71
5.0%
i 59
4.2%
t 49
3.5%
n 49
3.5%
o 49
3.5%
Other values (8) 190
13.5%

UX02[2]
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Please tell us on the scale below, whether you disagree or agree with the following statements. [The chatbot was useful to provide help to others]

Distinct 4
Distinct (%) 3.1%
Missing 10
Missing (%) 7.2%
Memory size 1.2 KiB
Agree
81
Strongly agree
36
Neither agree nor disagree
9
Disagree
2

Length

Max length 26
Median length 5
Mean length 9.0546875
Min length 5

Characters and Unicode

Total characters 1159
Distinct characters 18
Distinct categories 3 ?
Distinct scripts 2 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row Strongly agree
2nd row Neither agree nor disagree
3rd row Agree
4th row Strongly agree
5th row Agree

Common Values

Value Count Frequency (%)
Agree 81
58.7%
Strongly agree 36
26.1%
Neither agree nor disagree 9
6.5%
Disagree 2
1.4%
(Missing) 10
7.2%

Length

2022-07-04T20:23:45.774173 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:23:46.019805 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
agree 126
66.0%
strongly 36
18.8%
disagree 11
5.8%
neither 9
4.7%
nor 9
4.7%

Most occurring characters

Value Count Frequency (%)
e 292
25.2%
r 191
16.5%
g 173
14.9%
A 81
7.0%
63
5.4%
a 56
4.8%
t 45
3.9%
o 45
3.9%
n 45
3.9%
y 36
3.1%
Other values (8) 132
11.4%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 968
83.5%
Uppercase Letter 128
11.0%
Space Separator 63
5.4%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
e 292
30.2%
r 191
19.7%
g 173
17.9%
a 56
5.8%
t 45
4.6%
o 45
4.6%
n 45
4.6%
y 36
3.7%
l 36
3.7%
i 20
2.1%
Other values (3) 29
3.0%
Uppercase Letter
Value Count Frequency (%)
A 81
63.3%
S 36
28.1%
N 9
7.0%
D 2
1.6%
Space Separator
Value Count Frequency (%)
63
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 1096
94.6%
Common 63
5.4%

Most frequent character per script

Latin
Value Count Frequency (%)
e 292
26.6%
r 191
17.4%
g 173
15.8%
A 81
7.4%
a 56
5.1%
t 45
4.1%
o 45
4.1%
n 45
4.1%
y 36
3.3%
l 36
3.3%
Other values (7) 96
8.8%
Common
Value Count Frequency (%)
63
100.0%

Most occurring blocks

Value Count Frequency (%)
ASCII 1159
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
e 292
25.2%
r 191
16.5%
g 173
14.9%
A 81
7.0%
63
5.4%
a 56
4.8%
t 45
3.9%
o 45
3.9%
n 45
3.9%
y 36
3.1%
Other values (8) 132
11.4%

UX02[3]
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Please tell us on the scale below, whether you disagree or agree with the following statements. [I found the chatbot useful to get to know other students ]

Distinct 5
Distinct (%) 3.9%
Missing 10
Missing (%) 7.2%
Memory size 1.2 KiB
Agree
44
Disagree
30
Neither agree nor disagree
27
Strongly agree
16
Strongly disagree
11

Length

Max length 26
Median length 17
Mean length 12.2890625
Min length 5

Characters and Unicode

Total characters 1573
Distinct characters 18
Distinct categories 3 ?
Distinct scripts 2 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row Strongly agree
2nd row Strongly agree
3rd row Neither agree nor disagree
4th row Agree
5th row Agree

Common Values

Value Count Frequency (%)
Agree 44
31.9%
Disagree 30
21.7%
Neither agree nor disagree 27
19.6%
Strongly agree 16
11.6%
Strongly disagree 11
8.0%
(Missing) 10
7.2%

Length

2022-07-04T20:23:46.244003 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:23:46.496632 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
agree 87
36.9%
disagree 68
28.8%
neither 27
11.4%
nor 27
11.4%
strongly 27
11.4%

Most occurring characters

Value Count Frequency (%)
e 364
23.1%
r 236
15.0%
g 182
11.6%
a 111
7.1%
108
6.9%
i 95
6.0%
s 68
4.3%
t 54
3.4%
o 54
3.4%
n 54
3.4%
Other values (8) 247
15.7%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 1337
85.0%
Uppercase Letter 128
8.1%
Space Separator 108
6.9%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
e 364
27.2%
r 236
17.7%
g 182
13.6%
a 111
8.3%
i 95
7.1%
s 68
5.1%
t 54
4.0%
o 54
4.0%
n 54
4.0%
d 38
2.8%
Other values (3) 81
6.1%
Uppercase Letter
Value Count Frequency (%)
A 44
34.4%
D 30
23.4%
N 27
21.1%
S 27
21.1%
Space Separator
Value Count Frequency (%)
108
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 1465
93.1%
Common 108
6.9%

Most frequent character per script

Latin
Value Count Frequency (%)
e 364
24.8%
r 236
16.1%
g 182
12.4%
a 111
7.6%
i 95
6.5%
s 68
4.6%
t 54
3.7%
o 54
3.7%
n 54
3.7%
A 44
3.0%
Other values (7) 203
13.9%
Common
Value Count Frequency (%)
108
100.0%

Most occurring blocks

Value Count Frequency (%)
ASCII 1573
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
e 364
23.1%
r 236
15.0%
g 182
11.6%
a 111
7.1%
108
6.9%
i 95
6.0%
s 68
4.3%
t 54
3.4%
o 54
3.4%
n 54
3.4%
Other values (8) 247
15.7%

UX02[4]
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Please tell us on the scale below, whether you disagree or agree with the following statements. [I found the chatbot useful to make me feel part of a community]

Distinct 5
Distinct (%) 3.9%
Missing 10
Missing (%) 7.2%
Memory size 1.2 KiB
Agree
47
Strongly agree
30
Neither agree nor disagree
28
Disagree
18
Strongly disagree
5

Length

Max length 26
Median length 17
Mean length 12.59375
Min length 5

Characters and Unicode

Total characters 1612
Distinct characters 18
Distinct categories 3 ?
Distinct scripts 2 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row Strongly agree
2nd row Strongly agree
3rd row Disagree
4th row Strongly agree
5th row Strongly agree

Common Values

Value Count Frequency (%)
Agree 47
34.1%
Strongly agree 30
21.7%
Neither agree nor disagree 28
20.3%
Disagree 18
13.0%
Strongly disagree 5
3.6%
(Missing) 10
7.2%

Length

2022-07-04T20:23:46.752224 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:23:47.007557 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
agree 105
42.5%
disagree 51
20.6%
strongly 35
14.2%
neither 28
11.3%
nor 28
11.3%

Most occurring characters

Value Count Frequency (%)
e 368
22.8%
r 247
15.3%
g 191
11.8%
119
7.4%
a 109
6.8%
i 79
4.9%
t 63
3.9%
o 63
3.9%
n 63
3.9%
s 51
3.2%
Other values (8) 259
16.1%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 1365
84.7%
Uppercase Letter 128
7.9%
Space Separator 119
7.4%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
e 368
27.0%
r 247
18.1%
g 191
14.0%
a 109
8.0%
i 79
5.8%
t 63
4.6%
o 63
4.6%
n 63
4.6%
s 51
3.7%
y 35
2.6%
Other values (3) 96
7.0%
Uppercase Letter
Value Count Frequency (%)
A 47
36.7%
S 35
27.3%
N 28
21.9%
D 18
14.1%
Space Separator
Value Count Frequency (%)
119
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 1493
92.6%
Common 119
7.4%

Most frequent character per script

Latin
Value Count Frequency (%)
e 368
24.6%
r 247
16.5%
g 191
12.8%
a 109
7.3%
i 79
5.3%
t 63
4.2%
o 63
4.2%
n 63
4.2%
s 51
3.4%
A 47
3.1%
Other values (7) 212
14.2%
Common
Value Count Frequency (%)
119
100.0%

Most occurring blocks

Value Count Frequency (%)
ASCII 1612
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
e 368
22.8%
r 247
15.3%
g 191
11.8%
119
7.4%
a 109
6.8%
i 79
4.9%
t 63
3.9%
o 63
3.9%
n 63
3.9%
s 51
3.2%
Other values (8) 259
16.1%

UX02[5]
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Please tell us on the scale below, whether you disagree or agree with the following statements. [I felt comfortable using the chatbot to ask questions]

Distinct 5
Distinct (%) 3.9%
Missing 10
Missing (%) 7.2%
Memory size 1.2 KiB
Agree
64
Strongly agree
37
Neither agree nor disagree
20
Disagree
4
Strongly disagree
3

Length

Max length 26
Median length 21.5
Mean length 11.2578125
Min length 5

Characters and Unicode

Total characters 1441
Distinct characters 18
Distinct categories 3 ?
Distinct scripts 2 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row Strongly agree
2nd row Strongly agree
3rd row Agree
4th row Strongly agree
5th row Strongly agree

Common Values

Value Count Frequency (%)
Agree 64
46.4%
Strongly agree 37
26.8%
Neither agree nor disagree 20
14.5%
Disagree 4
2.9%
Strongly disagree 3
2.2%
(Missing) 10
7.2%

Length

2022-07-04T20:23:47.261551 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:23:47.510636 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
agree 121
53.1%
strongly 40
17.5%
disagree 27
11.8%
neither 20
8.8%
nor 20
8.8%

Most occurring characters

Value Count Frequency (%)
e 336
23.3%
r 228
15.8%
g 188
13.0%
100
6.9%
a 84
5.8%
A 64
4.4%
t 60
4.2%
o 60
4.2%
n 60
4.2%
i 47
3.3%
Other values (8) 214
14.9%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 1213
84.2%
Uppercase Letter 128
8.9%
Space Separator 100
6.9%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
e 336
27.7%
r 228
18.8%
g 188
15.5%
a 84
6.9%
t 60
4.9%
o 60
4.9%
n 60
4.9%
i 47
3.9%
y 40
3.3%
l 40
3.3%
Other values (3) 70
5.8%
Uppercase Letter
Value Count Frequency (%)
A 64
50.0%
S 40
31.2%
N 20
15.6%
D 4
3.1%
Space Separator
Value Count Frequency (%)
100
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 1341
93.1%
Common 100
6.9%

Most frequent character per script

Latin
Value Count Frequency (%)
e 336
25.1%
r 228
17.0%
g 188
14.0%
a 84
6.3%
A 64
4.8%
t 60
4.5%
o 60
4.5%
n 60
4.5%
i 47
3.5%
y 40
3.0%
Other values (7) 174
13.0%
Common
Value Count Frequency (%)
100
100.0%

Most occurring blocks

Value Count Frequency (%)
ASCII 1441
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
e 336
23.3%
r 228
15.8%
g 188
13.0%
100
6.9%
a 84
5.8%
A 64
4.4%
t 60
4.2%
o 60
4.2%
n 60
4.2%
i 47
3.3%
Other values (8) 214
14.9%

UX02[6]
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Please tell us on the scale below, whether you disagree or agree with the following statements. [I felt comfortable using the chatbot to answer questions]

Distinct 5
Distinct (%) 3.9%
Missing 10
Missing (%) 7.2%
Memory size 1.2 KiB
Strongly agree
57
Agree
57
Neither agree nor disagree
9
Disagree
4
Strongly disagree
1

Length

Max length 26
Median length 17
Mean length 10.671875
Min length 5

Characters and Unicode

Total characters 1366
Distinct characters 18
Distinct categories 3 ?
Distinct scripts 2 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 1 ?
Unique (%) 0.8%

Sample

1st row Strongly agree
2nd row Strongly agree
3rd row Agree
4th row Strongly agree
5th row Agree

Common Values

Value Count Frequency (%)
Strongly agree 57
41.3%
Agree 57
41.3%
Neither agree nor disagree 9
6.5%
Disagree 4
2.9%
Strongly disagree 1
0.7%
(Missing) 10
7.2%

Length

2022-07-04T20:23:47.749327 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:23:47.988640 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
agree 123
57.7%
strongly 58
27.2%
disagree 14
6.6%
neither 9
4.2%
nor 9
4.2%

Most occurring characters

Value Count Frequency (%)
e 292
21.4%
r 213
15.6%
g 195
14.3%
85
6.2%
a 80
5.9%
o 67
4.9%
n 67
4.9%
t 67
4.9%
S 58
4.2%
y 58
4.2%
Other values (8) 184
13.5%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 1153
84.4%
Uppercase Letter 128
9.4%
Space Separator 85
6.2%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
e 292
25.3%
r 213
18.5%
g 195
16.9%
a 80
6.9%
o 67
5.8%
n 67
5.8%
t 67
5.8%
y 58
5.0%
l 58
5.0%
i 23
2.0%
Other values (3) 33
2.9%
Uppercase Letter
Value Count Frequency (%)
S 58
45.3%
A 57
44.5%
N 9
7.0%
D 4
3.1%
Space Separator
Value Count Frequency (%)
85
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 1281
93.8%
Common 85
6.2%

Most frequent character per script

Latin
Value Count Frequency (%)
e 292
22.8%
r 213
16.6%
g 195
15.2%
a 80
6.2%
o 67
5.2%
n 67
5.2%
t 67
5.2%
S 58
4.5%
y 58
4.5%
l 58
4.5%
Other values (7) 126
9.8%
Common
Value Count Frequency (%)
85
100.0%

Most occurring blocks

Value Count Frequency (%)
ASCII 1366
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
e 292
21.4%
r 213
15.6%
g 195
14.3%
85
6.2%
a 80
5.9%
o 67
4.9%
n 67
4.9%
t 67
4.9%
S 58
4.2%
y 58
4.2%
Other values (8) 184
13.5%

UX02[7]
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Please tell us on the scale below, whether you disagree or agree with the following statements. [I felt pleased to be able to provide an answer]

Distinct 5
Distinct (%) 3.9%
Missing 10
Missing (%) 7.2%
Memory size 1.2 KiB
Agree
61
Strongly agree
57
Neither agree nor disagree
8
Strongly disagree
1
Disagree
1

Length

Max length 26
Median length 17
Mean length 10.4375
Min length 5

Characters and Unicode

Total characters 1336
Distinct characters 18
Distinct categories 3 ?
Distinct scripts 2 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 2 ?
Unique (%) 1.6%

Sample

1st row Strongly agree
2nd row Strongly agree
3rd row Neither agree nor disagree
4th row Strongly agree
5th row Agree

Common Values

Value Count Frequency (%)
Agree 61
44.2%
Strongly agree 57
41.3%
Neither agree nor disagree 8
5.8%
Strongly disagree 1
0.7%
Disagree 1
0.7%
(Missing) 10
7.2%

Length

2022-07-04T20:23:48.219474 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:23:48.470951 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
agree 126
60.0%
strongly 58
27.6%
disagree 10
4.8%
neither 8
3.8%
nor 8
3.8%

Most occurring characters

Value Count Frequency (%)
e 288
21.6%
r 210
15.7%
g 194
14.5%
82
6.1%
a 75
5.6%
t 66
4.9%
o 66
4.9%
n 66
4.9%
A 61
4.6%
y 58
4.3%
Other values (8) 170
12.7%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 1126
84.3%
Uppercase Letter 128
9.6%
Space Separator 82
6.1%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
e 288
25.6%
r 210
18.7%
g 194
17.2%
a 75
6.7%
t 66
5.9%
o 66
5.9%
n 66
5.9%
y 58
5.2%
l 58
5.2%
i 18
1.6%
Other values (3) 27
2.4%
Uppercase Letter
Value Count Frequency (%)
A 61
47.7%
S 58
45.3%
N 8
6.2%
D 1
0.8%
Space Separator
Value Count Frequency (%)
82
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 1254
93.9%
Common 82
6.1%

Most frequent character per script

Latin
Value Count Frequency (%)
e 288
23.0%
r 210
16.7%
g 194
15.5%
a 75
6.0%
t 66
5.3%
o 66
5.3%
n 66
5.3%
A 61
4.9%
y 58
4.6%
l 58
4.6%
Other values (7) 112
8.9%
Common
Value Count Frequency (%)
82
100.0%

Most occurring blocks

Value Count Frequency (%)
ASCII 1336
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
e 288
21.6%
r 210
15.7%
g 194
14.5%
82
6.1%
a 75
5.6%
t 66
4.9%
o 66
4.9%
n 66
4.9%
A 61
4.6%
y 58
4.3%
Other values (8) 170
12.7%

UX02[8]
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Please tell us on the scale below, whether you disagree or agree with the following statements. [I felt pleased to get answers to my questions]

Distinct 4
Distinct (%) 3.1%
Missing 10
Missing (%) 7.2%
Memory size 1.2 KiB
Agree
55
Strongly agree
54
Neither agree nor disagree
17
Disagree
2

Length

Max length 26
Median length 14
Mean length 11.6328125
Min length 5

Characters and Unicode

Total characters 1489
Distinct characters 18
Distinct categories 3 ?
Distinct scripts 2 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row Strongly agree
2nd row Strongly agree
3rd row Agree
4th row Strongly agree
5th row Strongly agree

Common Values

Value Count Frequency (%)
Agree 55
39.9%
Strongly agree 54
39.1%
Neither agree nor disagree 17
12.3%
Disagree 2
1.4%
(Missing) 10
7.2%

Length

2022-07-04T20:23:48.703018 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:23:48.936969 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
agree 126
54.1%
strongly 54
23.2%
disagree 19
8.2%
neither 17
7.3%
nor 17
7.3%

Most occurring characters

Value Count Frequency (%)
e 324
21.8%
r 233
15.6%
g 199
13.4%
105
7.1%
a 90
6.0%
t 71
4.8%
o 71
4.8%
n 71
4.8%
A 55
3.7%
y 54
3.6%
Other values (8) 216
14.5%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 1256
84.4%
Uppercase Letter 128
8.6%
Space Separator 105
7.1%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
e 324
25.8%
r 233
18.6%
g 199
15.8%
a 90
7.2%
t 71
5.7%
o 71
5.7%
n 71
5.7%
y 54
4.3%
l 54
4.3%
i 36
2.9%
Other values (3) 53
4.2%
Uppercase Letter
Value Count Frequency (%)
A 55
43.0%
S 54
42.2%
N 17
13.3%
D 2
1.6%
Space Separator
Value Count Frequency (%)
105
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 1384
92.9%
Common 105
7.1%

Most frequent character per script

Latin
Value Count Frequency (%)
e 324
23.4%
r 233
16.8%
g 199
14.4%
a 90
6.5%
t 71
5.1%
o 71
5.1%
n 71
5.1%
A 55
4.0%
y 54
3.9%
l 54
3.9%
Other values (7) 162
11.7%
Common
Value Count Frequency (%)
105
100.0%

Most occurring blocks

Value Count Frequency (%)
ASCII 1489
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
e 324
21.8%
r 233
15.6%
g 199
13.4%
105
7.1%
a 90
6.0%
t 71
4.8%
o 71
4.8%
n 71
4.8%
A 55
3.7%
y 54
3.6%
Other values (8) 216
14.5%

UX02[9]
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Please tell us on the scale below, whether you disagree or agree with the following statements. [I found the chatbot trustworthy ]

Distinct 5
Distinct (%) 3.9%
Missing 10
Missing (%) 7.2%
Memory size 1.2 KiB
Agree
70
Neither agree nor disagree
26
Strongly agree
24
Disagree
6
Strongly disagree
2

Length

Max length 26
Median length 5
Mean length 11.28125
Min length 5

Characters and Unicode

Total characters 1444
Distinct characters 18
Distinct categories 3 ?
Distinct scripts 2 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row Strongly agree
2nd row Strongly agree
3rd row Neither agree nor disagree
4th row Strongly agree
5th row Agree

Common Values

Value Count Frequency (%)
Agree 70
50.7%
Neither agree nor disagree 26
18.8%
Strongly agree 24
17.4%
Disagree 6
4.3%
Strongly disagree 2
1.4%
(Missing) 10
7.2%

Length

2022-07-04T20:23:49.169499 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:23:49.412224 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
agree 120
51.7%
disagree 34
14.7%
neither 26
11.2%
nor 26
11.2%
strongly 26
11.2%

Most occurring characters

Value Count Frequency (%)
e 360
24.9%
r 232
16.1%
g 180
12.5%
104
7.2%
a 84
5.8%
A 70
4.8%
i 60
4.2%
t 52
3.6%
n 52
3.6%
o 52
3.6%
Other values (8) 198
13.7%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 1212
83.9%
Uppercase Letter 128
8.9%
Space Separator 104
7.2%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
e 360
29.7%
r 232
19.1%
g 180
14.9%
a 84
6.9%
i 60
5.0%
t 52
4.3%
n 52
4.3%
o 52
4.3%
s 34
2.8%
d 28
2.3%
Other values (3) 78
6.4%
Uppercase Letter
Value Count Frequency (%)
A 70
54.7%
N 26
20.3%
S 26
20.3%
D 6
4.7%
Space Separator
Value Count Frequency (%)
104
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 1340
92.8%
Common 104
7.2%

Most frequent character per script

Latin
Value Count Frequency (%)
e 360
26.9%
r 232
17.3%
g 180
13.4%
a 84
6.3%
A 70
5.2%
i 60
4.5%
t 52
3.9%
n 52
3.9%
o 52
3.9%
s 34
2.5%
Other values (7) 164
12.2%
Common
Value Count Frequency (%)
104
100.0%

Most occurring blocks

Value Count Frequency (%)
ASCII 1444
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
e 360
24.9%
r 232
16.1%
g 180
12.5%
104
7.2%
a 84
5.8%
A 70
4.8%
i 60
4.2%
t 52
3.6%
n 52
3.6%
o 52
3.6%
Other values (8) 198
13.7%

UX02[10]
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Please tell us on the scale below, whether you disagree or agree with the following statements. [I would keep using the chatbot in my everyday life]

Distinct 5
Distinct (%) 3.9%
Missing 10
Missing (%) 7.2%
Memory size 1.2 KiB
Agree
52
Neither agree nor disagree
37
Disagree
21
Strongly agree
17
Strongly disagree
1

Length

Max length 26
Median length 17
Mean length 12.8515625
Min length 5

Characters and Unicode

Total characters 1645
Distinct characters 18
Distinct categories 3 ?
Distinct scripts 2 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 1 ?
Unique (%) 0.8%

Sample

1st row Strongly agree
2nd row Strongly agree
3rd row Neither agree nor disagree
4th row Agree
5th row Strongly agree

Common Values

Value Count Frequency (%)
Agree 52
37.7%
Neither agree nor disagree 37
26.8%
Disagree 21
15.2%
Strongly agree 17
12.3%
Strongly disagree 1
0.7%
(Missing) 10
7.2%

Length

2022-07-04T20:23:49.653705 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:23:49.904094 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
agree 106
41.2%
disagree 59
23.0%
neither 37
14.4%
nor 37
14.4%
strongly 18
7.0%

Most occurring characters

Value Count Frequency (%)
e 404
24.6%
r 257
15.6%
g 183
11.1%
129
7.8%
a 113
6.9%
i 96
5.8%
s 59
3.6%
n 55
3.3%
t 55
3.3%
o 55
3.3%
Other values (8) 239
14.5%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 1388
84.4%
Space Separator 129
7.8%
Uppercase Letter 128
7.8%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
e 404
29.1%
r 257
18.5%
g 183
13.2%
a 113
8.1%
i 96
6.9%
s 59
4.3%
n 55
4.0%
t 55
4.0%
o 55
4.0%
d 38
2.7%
Other values (3) 73
5.3%
Uppercase Letter
Value Count Frequency (%)
A 52
40.6%
N 37
28.9%
D 21
16.4%
S 18
14.1%
Space Separator
Value Count Frequency (%)
129
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 1516
92.2%
Common 129
7.8%

Most frequent character per script

Latin
Value Count Frequency (%)
e 404
26.6%
r 257
17.0%
g 183
12.1%
a 113
7.5%
i 96
6.3%
s 59
3.9%
n 55
3.6%
t 55
3.6%
o 55
3.6%
A 52
3.4%
Other values (7) 187
12.3%
Common
Value Count Frequency (%)
129
100.0%

Most occurring blocks

Value Count Frequency (%)
ASCII 1645
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
e 404
24.6%
r 257
15.6%
g 183
11.1%
129
7.8%
a 113
6.9%
i 96
5.8%
s 59
3.6%
n 55
3.3%
t 55
3.3%
o 55
3.3%
Other values (8) 239
14.5%

UX02[11]
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Please tell us on the scale below, whether you disagree or agree with the following statements. [I felt I was able to answer the questions I received]

Distinct 5
Distinct (%) 3.9%
Missing 10
Missing (%) 7.2%
Memory size 1.2 KiB
Agree
64
Strongly agree
21
Neither agree nor disagree
20
Strongly disagree
16
Disagree
7

Length

Max length 26
Median length 21.5
Mean length 11.421875
Min length 5

Characters and Unicode

Total characters 1462
Distinct characters 18
Distinct categories 3 ?
Distinct scripts 2 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row Agree
2nd row Strongly agree
3rd row Agree
4th row Strongly agree
5th row Strongly agree

Common Values

Value Count Frequency (%)
Agree 64
46.4%
Strongly agree 21
15.2%
Neither agree nor disagree 20
14.5%
Strongly disagree 16
11.6%
Disagree 7
5.1%
(Missing) 10
7.2%

Length

2022-07-04T20:23:50.147479 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:23:50.391315 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
agree 105
46.7%
disagree 43
19.1%
strongly 37
16.4%
neither 20
8.9%
nor 20
8.9%

Most occurring characters

Value Count Frequency (%)
e 336
23.0%
r 225
15.4%
g 185
12.7%
97
6.6%
a 84
5.7%
A 64
4.4%
i 63
4.3%
t 57
3.9%
o 57
3.9%
n 57
3.9%
Other values (8) 237
16.2%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 1237
84.6%
Uppercase Letter 128
8.8%
Space Separator 97
6.6%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
e 336
27.2%
r 225
18.2%
g 185
15.0%
a 84
6.8%
i 63
5.1%
t 57
4.6%
o 57
4.6%
n 57
4.6%
s 43
3.5%
y 37
3.0%
Other values (3) 93
7.5%
Uppercase Letter
Value Count Frequency (%)
A 64
50.0%
S 37
28.9%
N 20
15.6%
D 7
5.5%
Space Separator
Value Count Frequency (%)
97
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 1365
93.4%
Common 97
6.6%

Most frequent character per script

Latin
Value Count Frequency (%)
e 336
24.6%
r 225
16.5%
g 185
13.6%
a 84
6.2%
A 64
4.7%
i 63
4.6%
t 57
4.2%
o 57
4.2%
n 57
4.2%
s 43
3.2%
Other values (7) 194
14.2%
Common
Value Count Frequency (%)
97
100.0%

Most occurring blocks

Value Count Frequency (%)
ASCII 1462
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
e 336
23.0%
r 225
15.4%
g 185
12.7%
97
6.6%
a 84
5.7%
A 64
4.4%
i 63
4.3%
t 57
3.9%
o 57
3.9%
n 57
3.9%
Other values (8) 237
16.2%

UX02[12]
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Please tell us on the scale below, whether you disagree or agree with the following statements. [The short communication style (1 question-1 answer) was a bit limiting]

Distinct 4
Distinct (%) 3.1%
Missing 10
Missing (%) 7.2%
Memory size 1.2 KiB
Agree
56
Strongly agree
42
Disagree
16
Neither agree nor disagree
14

Length

Max length 26
Median length 14
Mean length 10.625
Min length 5

Characters and Unicode

Total characters 1360
Distinct characters 18
Distinct categories 3 ?
Distinct scripts 2 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row Agree
2nd row Strongly agree
3rd row Strongly agree
4th row Agree
5th row Agree

Common Values

Value Count Frequency (%)
Agree 56
40.6%
Strongly agree 42
30.4%
Disagree 16
11.6%
Neither agree nor disagree 14
10.1%
(Missing) 10
7.2%

Length

2022-07-04T20:23:50.867025 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:23:51.107131 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
agree 112
52.8%
strongly 42
19.8%
disagree 30
14.2%
neither 14
6.6%
nor 14
6.6%

Most occurring characters

Value Count Frequency (%)
e 312
22.9%
r 212
15.6%
g 184
13.5%
a 86
6.3%
84
6.2%
A 56
4.1%
t 56
4.1%
o 56
4.1%
n 56
4.1%
i 44
3.2%
Other values (8) 214
15.7%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 1148
84.4%
Uppercase Letter 128
9.4%
Space Separator 84
6.2%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
e 312
27.2%
r 212
18.5%
g 184
16.0%
a 86
7.5%
t 56
4.9%
o 56
4.9%
n 56
4.9%
i 44
3.8%
y 42
3.7%
l 42
3.7%
Other values (3) 58
5.1%
Uppercase Letter
Value Count Frequency (%)
A 56
43.8%
S 42
32.8%
D 16
12.5%
N 14
10.9%
Space Separator
Value Count Frequency (%)
84
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 1276
93.8%
Common 84
6.2%

Most frequent character per script

Latin
Value Count Frequency (%)
e 312
24.5%
r 212
16.6%
g 184
14.4%
a 86
6.7%
A 56
4.4%
t 56
4.4%
o 56
4.4%
n 56
4.4%
i 44
3.4%
y 42
3.3%
Other values (7) 172
13.5%
Common
Value Count Frequency (%)
84
100.0%

Most occurring blocks

Value Count Frequency (%)
ASCII 1360
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
e 312
22.9%
r 212
15.6%
g 184
13.5%
a 86
6.3%
84
6.2%
A 56
4.1%
t 56
4.1%
o 56
4.1%
n 56
4.1%
i 44
3.2%
Other values (8) 214
15.7%

UX02[13]
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Please tell us on the scale below, whether you disagree or agree with the following statements. [I'd have liked to see others' interactions]

Distinct 5
Distinct (%) 3.9%
Missing 10
Missing (%) 7.2%
Memory size 1.2 KiB
Agree
57
Strongly agree
50
Neither agree nor disagree
17
Disagree
3
Strongly disagree
1

Length

Max length 26
Median length 17
Mean length 11.46875
Min length 5

Characters and Unicode

Total characters 1468
Distinct characters 18
Distinct categories 3 ?
Distinct scripts 2 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 1 ?
Unique (%) 0.8%

Sample

1st row Agree
2nd row Strongly agree
3rd row Agree
4th row Strongly agree
5th row Strongly agree

Common Values

Value Count Frequency (%)
Agree 57
41.3%
Strongly agree 50
36.2%
Neither agree nor disagree 17
12.3%
Disagree 3
2.2%
Strongly disagree 1
0.7%
(Missing) 10
7.2%

Length

2022-07-04T20:23:51.339018 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:23:51.588771 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
agree 124
53.9%
strongly 51
22.2%
disagree 21
9.1%
neither 17
7.4%
nor 17
7.4%

Most occurring characters

Value Count Frequency (%)
e 324
22.1%
r 230
15.7%
g 196
13.4%
102
6.9%
a 88
6.0%
t 68
4.6%
o 68
4.6%
n 68
4.6%
A 57
3.9%
y 51
3.5%
Other values (8) 216
14.7%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 1238
84.3%
Uppercase Letter 128
8.7%
Space Separator 102
6.9%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
e 324
26.2%
r 230
18.6%
g 196
15.8%
a 88
7.1%
t 68
5.5%
o 68
5.5%
n 68
5.5%
y 51
4.1%
l 51
4.1%
i 38
3.1%
Other values (3) 56
4.5%
Uppercase Letter
Value Count Frequency (%)
A 57
44.5%
S 51
39.8%
N 17
13.3%
D 3
2.3%
Space Separator
Value Count Frequency (%)
102
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 1366
93.1%
Common 102
6.9%

Most frequent character per script

Latin
Value Count Frequency (%)
e 324
23.7%
r 230
16.8%
g 196
14.3%
a 88
6.4%
t 68
5.0%
o 68
5.0%
n 68
5.0%
A 57
4.2%
y 51
3.7%
l 51
3.7%
Other values (7) 165
12.1%
Common
Value Count Frequency (%)
102
100.0%

Most occurring blocks

Value Count Frequency (%)
ASCII 1468
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
e 324
22.1%
r 230
15.7%
g 196
13.4%
102
6.9%
a 88
6.0%
t 68
4.6%
o 68
4.6%
n 68
4.6%
A 57
3.9%
y 51
3.5%
Other values (8) 216
14.7%

UX02[14]
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Please tell us on the scale below, whether you disagree or agree with the following statements. [I’d have liked more answers to my questions]

Distinct 5
Distinct (%) 3.9%
Missing 10
Missing (%) 7.2%
Memory size 1.2 KiB
Agree
49
Strongly agree
41
Neither agree nor disagree
25
Disagree
10
Strongly disagree
3

Length

Max length 26
Median length 17
Mean length 12.5
Min length 5

Characters and Unicode

Total characters 1600
Distinct characters 18
Distinct categories 3 ?
Distinct scripts 2 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row Strongly agree
2nd row Strongly agree
3rd row Agree
4th row Strongly agree
5th row Strongly agree

Common Values

Value Count Frequency (%)
Agree 49
35.5%
Strongly agree 41
29.7%
Neither agree nor disagree 25
18.1%
Disagree 10
7.2%
Strongly disagree 3
2.2%
(Missing) 10
7.2%

Length

2022-07-04T20:23:51.829250 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:23:52.070911 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
agree 115
46.6%
strongly 44
17.8%
disagree 38
15.4%
neither 25
10.1%
nor 25
10.1%

Most occurring characters

Value Count Frequency (%)
e 356
22.2%
r 247
15.4%
g 197
12.3%
119
7.4%
a 104
6.5%
t 69
4.3%
o 69
4.3%
n 69
4.3%
i 63
3.9%
A 49
3.1%
Other values (8) 258
16.1%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 1353
84.6%
Uppercase Letter 128
8.0%
Space Separator 119
7.4%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
e 356
26.3%
r 247
18.3%
g 197
14.6%
a 104
7.7%
t 69
5.1%
o 69
5.1%
n 69
5.1%
i 63
4.7%
y 44
3.3%
l 44
3.3%
Other values (3) 91
6.7%
Uppercase Letter
Value Count Frequency (%)
A 49
38.3%
S 44
34.4%
N 25
19.5%
D 10
7.8%
Space Separator
Value Count Frequency (%)
119
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 1481
92.6%
Common 119
7.4%

Most frequent character per script

Latin
Value Count Frequency (%)
e 356
24.0%
r 247
16.7%
g 197
13.3%
a 104
7.0%
t 69
4.7%
o 69
4.7%
n 69
4.7%
i 63
4.3%
A 49
3.3%
y 44
3.0%
Other values (7) 214
14.4%
Common
Value Count Frequency (%)
119
100.0%

Most occurring blocks

Value Count Frequency (%)
ASCII 1600
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
e 356
22.2%
r 247
15.4%
g 197
12.3%
119
7.4%
a 104
6.5%
t 69
4.3%
o 69
4.3%
n 69
4.3%
i 63
3.9%
A 49
3.1%
Other values (8) 258
16.1%

UX02[15]
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Please tell us on the scale below, whether you disagree or agree with the following statements. [I played with the settings to try to get as diverse answers as possible]

Distinct 5
Distinct (%) 3.9%
Missing 10
Missing (%) 7.2%
Memory size 1.2 KiB
Agree
37
Disagree
29
Neither agree nor disagree
24
Strongly disagree
20
Strongly agree
18

Length

Max length 26
Median length 17
Mean length 12.7578125
Min length 5

Characters and Unicode

Total characters 1633
Distinct characters 18
Distinct categories 3 ?
Distinct scripts 2 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row Strongly agree
2nd row Strongly agree
3rd row Agree
4th row Disagree
5th row Strongly agree

Common Values

Value Count Frequency (%)
Agree 37
26.8%
Disagree 29
21.0%
Neither agree nor disagree 24
17.4%
Strongly disagree 20
14.5%
Strongly agree 18
13.0%
(Missing) 10
7.2%

Length

2022-07-04T20:23:52.305671 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:23:52.551632 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
agree 79
33.2%
disagree 73
30.7%
strongly 38
16.0%
neither 24
10.1%
nor 24
10.1%

Most occurring characters

Value Count Frequency (%)
e 352
21.6%
r 238
14.6%
g 190
11.6%
a 115
7.0%
110
6.7%
i 97
5.9%
s 73
4.5%
t 62
3.8%
o 62
3.8%
n 62
3.8%
Other values (8) 272
16.7%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 1395
85.4%
Uppercase Letter 128
7.8%
Space Separator 110
6.7%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
e 352
25.2%
r 238
17.1%
g 190
13.6%
a 115
8.2%
i 97
7.0%
s 73
5.2%
t 62
4.4%
o 62
4.4%
n 62
4.4%
d 44
3.2%
Other values (3) 100
7.2%
Uppercase Letter
Value Count Frequency (%)
S 38
29.7%
A 37
28.9%
D 29
22.7%
N 24
18.8%
Space Separator
Value Count Frequency (%)
110
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 1523
93.3%
Common 110
6.7%

Most frequent character per script

Latin
Value Count Frequency (%)
e 352
23.1%
r 238
15.6%
g 190
12.5%
a 115
7.6%
i 97
6.4%
s 73
4.8%
t 62
4.1%
o 62
4.1%
n 62
4.1%
d 44
2.9%
Other values (7) 228
15.0%
Common
Value Count Frequency (%)
110
100.0%

Most occurring blocks

Value Count Frequency (%)
ASCII 1633
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
e 352
21.6%
r 238
14.6%
g 190
11.6%
a 115
7.0%
110
6.7%
i 97
5.9%
s 73
4.5%
t 62
3.8%
o 62
3.8%
n 62
3.8%
Other values (8) 272
16.7%

UX02[16]
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Please tell us on the scale below, whether you disagree or agree with the following statements. [Using chatbot in this university would benefit students ]

Distinct 5
Distinct (%) 3.9%
Missing 10
Missing (%) 7.2%
Memory size 1.2 KiB
Agree
67
Strongly agree
34
Neither agree nor disagree
22
Disagree
3
Strongly disagree
2

Length

Max length 26
Median length 5
Mean length 11.2578125
Min length 5

Characters and Unicode

Total characters 1441
Distinct characters 18
Distinct categories 3 ?
Distinct scripts 2 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row Strongly agree
2nd row Strongly agree
3rd row Neither agree nor disagree
4th row Agree
5th row Agree

Common Values

Value Count Frequency (%)
Agree 67
48.6%
Strongly agree 34
24.6%
Neither agree nor disagree 22
15.9%
Disagree 3
2.2%
Strongly disagree 2
1.4%
(Missing) 10
7.2%

Length

2022-07-04T20:23:52.802838 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:23:53.048321 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
agree 123
53.5%
strongly 36
15.7%
disagree 27
11.7%
neither 22
9.6%
nor 22
9.6%

Most occurring characters

Value Count Frequency (%)
e 344
23.9%
r 230
16.0%
g 186
12.9%
102
7.1%
a 83
5.8%
A 67
4.6%
t 58
4.0%
o 58
4.0%
n 58
4.0%
i 49
3.4%
Other values (8) 206
14.3%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 1211
84.0%
Uppercase Letter 128
8.9%
Space Separator 102
7.1%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
e 344
28.4%
r 230
19.0%
g 186
15.4%
a 83
6.9%
t 58
4.8%
o 58
4.8%
n 58
4.8%
i 49
4.0%
y 36
3.0%
l 36
3.0%
Other values (3) 73
6.0%
Uppercase Letter
Value Count Frequency (%)
A 67
52.3%
S 36
28.1%
N 22
17.2%
D 3
2.3%
Space Separator
Value Count Frequency (%)
102
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 1339
92.9%
Common 102
7.1%

Most frequent character per script

Latin
Value Count Frequency (%)
e 344
25.7%
r 230
17.2%
g 186
13.9%
a 83
6.2%
A 67
5.0%
t 58
4.3%
o 58
4.3%
n 58
4.3%
i 49
3.7%
y 36
2.7%
Other values (7) 170
12.7%
Common
Value Count Frequency (%)
102
100.0%

Most occurring blocks

Value Count Frequency (%)
ASCII 1441
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
e 344
23.9%
r 230
16.0%
g 186
12.9%
102
7.1%
a 83
5.8%
A 67
4.6%
t 58
4.0%
o 58
4.0%
n 58
4.0%
i 49
3.4%
Other values (8) 206
14.3%

B01
Unsupported

MISSING
REJECTED
UNSUPPORTED

The chatbot gave badges as people used the application.  To help us improve the badges in future versions please answer the following questions. Here is an example badge: “Congratulations! You just earned the First Question badge! Way to go!”

Missing 138
Missing (%) 100.0%
Memory size 1.2 KiB

B03[1]
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Please indicate whether you agree or disagree with these statements. [I liked the chatbot's badges]

Distinct 5
Distinct (%) 3.9%
Missing 11
Missing (%) 8.0%
Memory size 1.2 KiB
Agree
58
Strongly agree
37
Neither agree nor disagree
20
Disagree
9
Strongly disagree
3

Length

Max length 26
Median length 17
Mean length 11.42519685
Min length 5

Characters and Unicode

Total characters 1451
Distinct characters 18
Distinct categories 3 ?
Distinct scripts 2 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row Strongly agree
2nd row Strongly agree
3rd row Disagree
4th row Strongly agree
5th row Strongly agree

Common Values

Value Count Frequency (%)
Agree 58
42.0%
Strongly agree 37
26.8%
Neither agree nor disagree 20
14.5%
Disagree 9
6.5%
Strongly disagree 3
2.2%
(Missing) 11
8.0%

Length

2022-07-04T20:23:53.288947 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:23:53.536381 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
agree 115
50.7%
strongly 40
17.6%
disagree 32
14.1%
neither 20
8.8%
nor 20
8.8%

Most occurring characters

Value Count Frequency (%)
e 334
23.0%
r 227
15.6%
g 187
12.9%
100
6.9%
a 89
6.1%
t 60
4.1%
o 60
4.1%
n 60
4.1%
A 58
4.0%
i 52
3.6%
Other values (8) 224
15.4%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 1224
84.4%
Uppercase Letter 127
8.8%
Space Separator 100
6.9%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
e 334
27.3%
r 227
18.5%
g 187
15.3%
a 89
7.3%
t 60
4.9%
o 60
4.9%
n 60
4.9%
i 52
4.2%
y 40
3.3%
l 40
3.3%
Other values (3) 75
6.1%
Uppercase Letter
Value Count Frequency (%)
A 58
45.7%
S 40
31.5%
N 20
15.7%
D 9
7.1%
Space Separator
Value Count Frequency (%)
100
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 1351
93.1%
Common 100
6.9%

Most frequent character per script

Latin
Value Count Frequency (%)
e 334
24.7%
r 227
16.8%
g 187
13.8%
a 89
6.6%
t 60
4.4%
o 60
4.4%
n 60
4.4%
A 58
4.3%
i 52
3.8%
y 40
3.0%
Other values (7) 184
13.6%
Common
Value Count Frequency (%)
100
100.0%

Most occurring blocks

Value Count Frequency (%)
ASCII 1451
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
e 334
23.0%
r 227
15.6%
g 187
12.9%
100
6.9%
a 89
6.1%
t 60
4.1%
o 60
4.1%
n 60
4.1%
A 58
4.0%
i 52
3.6%
Other values (8) 224
15.4%

B03[2]
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Please indicate whether you agree or disagree with these statements. [The badges were a distraction]

Distinct 5
Distinct (%) 3.9%
Missing 11
Missing (%) 8.0%
Memory size 1.2 KiB
Disagree
62
Strongly disagree
27
Neither agree nor disagree
23
Agree
11
Strongly agree
4

Length

Max length 26
Median length 17
Mean length 13.1023622
Min length 5

Characters and Unicode

Total characters 1664
Distinct characters 18
Distinct categories 3 ?
Distinct scripts 2 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row Strongly disagree
2nd row Disagree
3rd row Agree
4th row Strongly disagree
5th row Disagree

Common Values

Value Count Frequency (%)
Disagree 62
44.9%
Strongly disagree 27
19.6%
Neither agree nor disagree 23
16.7%
Agree 11
8.0%
Strongly agree 4
2.9%
(Missing) 11
8.0%

Length

2022-07-04T20:23:53.776812 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:23:54.023544 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
disagree 112
49.3%
agree 38
16.7%
strongly 31
13.7%
neither 23
10.1%
nor 23
10.1%

Most occurring characters

Value Count Frequency (%)
e 346
20.8%
r 227
13.6%
g 181
10.9%
a 139
8.4%
i 135
8.1%
s 112
6.7%
100
6.0%
D 62
3.7%
n 54
3.2%
o 54
3.2%
Other values (8) 254
15.3%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 1437
86.4%
Uppercase Letter 127
7.6%
Space Separator 100
6.0%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
e 346
24.1%
r 227
15.8%
g 181
12.6%
a 139
9.7%
i 135
9.4%
s 112
7.8%
n 54
3.8%
o 54
3.8%
t 54
3.8%
d 50
3.5%
Other values (3) 85
5.9%
Uppercase Letter
Value Count Frequency (%)
D 62
48.8%
S 31
24.4%
N 23
18.1%
A 11
8.7%
Space Separator
Value Count Frequency (%)
100
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 1564
94.0%
Common 100
6.0%

Most frequent character per script

Latin
Value Count Frequency (%)
e 346
22.1%
r 227
14.5%
g 181
11.6%
a 139
8.9%
i 135
8.6%
s 112
7.2%
D 62
4.0%
n 54
3.5%
o 54
3.5%
t 54
3.5%
Other values (7) 200
12.8%
Common
Value Count Frequency (%)
100
100.0%

Most occurring blocks

Value Count Frequency (%)
ASCII 1664
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
e 346
20.8%
r 227
13.6%
g 181
10.9%
a 139
8.4%
i 135
8.1%
s 112
6.7%
100
6.0%
D 62
3.7%
n 54
3.2%
o 54
3.2%
Other values (8) 254
15.3%

B03[3]
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Please indicate whether you agree or disagree with these statements. [The badges encouraged me to contribute to the chatbot]

Distinct 5
Distinct (%) 3.9%
Missing 11
Missing (%) 8.0%
Memory size 1.2 KiB
Agree
52
Strongly agree
33
Neither agree nor disagree
19
Disagree
18
Strongly disagree
5

Length

Max length 26
Median length 17
Mean length 11.37795276
Min length 5

Characters and Unicode

Total characters 1445
Distinct characters 18
Distinct categories 3 ?
Distinct scripts 2 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row Strongly agree
2nd row Strongly agree
3rd row Disagree
4th row Strongly agree
5th row Strongly agree

Common Values

Value Count Frequency (%)
Agree 52
37.7%
Strongly agree 33
23.9%
Neither agree nor disagree 19
13.8%
Disagree 18
13.0%
Strongly disagree 5
3.6%
(Missing) 11
8.0%

Length

2022-07-04T20:23:54.267817 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:23:54.515043 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
agree 104
46.8%
disagree 42
18.9%
strongly 38
17.1%
neither 19
8.6%
nor 19
8.6%

Most occurring characters

Value Count Frequency (%)
e 330
22.8%
r 222
15.4%
g 184
12.7%
95
6.6%
a 94
6.5%
i 61
4.2%
t 57
3.9%
o 57
3.9%
n 57
3.9%
A 52
3.6%
Other values (8) 236
16.3%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 1223
84.6%
Uppercase Letter 127
8.8%
Space Separator 95
6.6%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
e 330
27.0%
r 222
18.2%
g 184
15.0%
a 94
7.7%
i 61
5.0%
t 57
4.7%
o 57
4.7%
n 57
4.7%
s 42
3.4%
y 38
3.1%
Other values (3) 81
6.6%
Uppercase Letter
Value Count Frequency (%)
A 52
40.9%
S 38
29.9%
N 19
15.0%
D 18
14.2%
Space Separator
Value Count Frequency (%)
95
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 1350
93.4%
Common 95
6.6%

Most frequent character per script

Latin
Value Count Frequency (%)
e 330
24.4%
r 222
16.4%
g 184
13.6%
a 94
7.0%
i 61
4.5%
t 57
4.2%
o 57
4.2%
n 57
4.2%
A 52
3.9%
s 42
3.1%
Other values (7) 194
14.4%
Common
Value Count Frequency (%)
95
100.0%

Most occurring blocks

Value Count Frequency (%)
ASCII 1445
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
e 330
22.8%
r 222
15.4%
g 184
12.7%
95
6.6%
a 94
6.5%
i 61
4.2%
t 57
3.9%
o 57
3.9%
n 57
3.9%
A 52
3.6%
Other values (8) 236
16.3%

B03[4]
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Please indicate whether you agree or disagree with these statements. [Chatbot should be more generous with badges]

Distinct 5
Distinct (%) 3.9%
Missing 11
Missing (%) 8.0%
Memory size 1.2 KiB
Neither agree nor disagree
68
Disagree
28
Agree
26
Strongly agree
4
Strongly disagree
1

Length

Max length 26
Median length 26
Mean length 17.28346457
Min length 5

Characters and Unicode

Total characters 2195
Distinct characters 18
Distinct categories 3 ?
Distinct scripts 2 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 1 ?
Unique (%) 0.8%

Sample

1st row Strongly agree
2nd row Agree
3rd row Neither agree nor disagree
4th row Strongly agree
5th row Neither agree nor disagree

Common Values

Value Count Frequency (%)
Neither agree nor disagree 68
49.3%
Disagree 28
20.3%
Agree 26
18.8%
Strongly agree 4
2.9%
Strongly disagree 1
0.7%
(Missing) 11
8.0%

Length

2022-07-04T20:23:54.766741 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:23:55.022234 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
agree 98
29.2%
disagree 97
28.9%
neither 68
20.2%
nor 68
20.2%
strongly 5
1.5%

Most occurring characters

Value Count Frequency (%)
e 526
24.0%
r 336
15.3%
209
9.5%
g 200
9.1%
a 169
7.7%
i 165
7.5%
s 97
4.4%
n 73
3.3%
t 73
3.3%
o 73
3.3%
Other values (8) 274
12.5%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 1859
84.7%
Space Separator 209
9.5%
Uppercase Letter 127
5.8%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
e 526
28.3%
r 336
18.1%
g 200
10.8%
a 169
9.1%
i 165
8.9%
s 97
5.2%
n 73
3.9%
t 73
3.9%
o 73
3.9%
d 69
3.7%
Other values (3) 78
4.2%
Uppercase Letter
Value Count Frequency (%)
N 68
53.5%
D 28
22.0%
A 26
20.5%
S 5
3.9%
Space Separator
Value Count Frequency (%)
209
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 1986
90.5%
Common 209
9.5%

Most frequent character per script

Latin
Value Count Frequency (%)
e 526
26.5%
r 336
16.9%
g 200
10.1%
a 169
8.5%
i 165
8.3%
s 97
4.9%
n 73
3.7%
t 73
3.7%
o 73
3.7%
d 69
3.5%
Other values (7) 205
10.3%
Common
Value Count Frequency (%)
209
100.0%

Most occurring blocks

Value Count Frequency (%)
ASCII 2195
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
e 526
24.0%
r 336
15.3%
209
9.5%
g 200
9.1%
a 169
7.7%
i 165
7.5%
s 97
4.4%
n 73
3.3%
t 73
3.3%
o 73
3.3%
Other values (8) 274
12.5%

B03[5]
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Please indicate whether you agree or disagree with these statements. [More type of badges should be used]

Distinct 5
Distinct (%) 3.9%
Missing 11
Missing (%) 8.0%
Memory size 1.2 KiB
Agree
47
Neither agree nor disagree
41
Strongly agree
22
Disagree
14
Strongly disagree
3

Length

Max length 26
Median length 17
Mean length 13.95275591
Min length 5

Characters and Unicode

Total characters 1772
Distinct characters 18
Distinct categories 3 ?
Distinct scripts 2 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row Strongly agree
2nd row Strongly agree
3rd row Disagree
4th row Disagree
5th row Strongly agree

Common Values

Value Count Frequency (%)
Agree 47
34.1%
Neither agree nor disagree 41
29.7%
Strongly agree 22
15.9%
Disagree 14
10.1%
Strongly disagree 3
2.2%
(Missing) 11
8.0%

Length

2022-07-04T20:23:55.267526 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:23:55.526048 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
agree 110
40.0%
disagree 58
21.1%
neither 41
14.9%
nor 41
14.9%
strongly 25
9.1%

Most occurring characters

Value Count Frequency (%)
e 418
23.6%
r 275
15.5%
g 193
10.9%
148
8.4%
a 121
6.8%
i 99
5.6%
n 66
3.7%
t 66
3.7%
o 66
3.7%
s 58
3.3%
Other values (8) 262
14.8%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 1497
84.5%
Space Separator 148
8.4%
Uppercase Letter 127
7.2%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
e 418
27.9%
r 275
18.4%
g 193
12.9%
a 121
8.1%
i 99
6.6%
n 66
4.4%
t 66
4.4%
o 66
4.4%
s 58
3.9%
d 44
2.9%
Other values (3) 91
6.1%
Uppercase Letter
Value Count Frequency (%)
A 47
37.0%
N 41
32.3%
S 25
19.7%
D 14
11.0%
Space Separator
Value Count Frequency (%)
148
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 1624
91.6%
Common 148
8.4%

Most frequent character per script

Latin
Value Count Frequency (%)
e 418
25.7%
r 275
16.9%
g 193
11.9%
a 121
7.5%
i 99
6.1%
n 66
4.1%
t 66
4.1%
o 66
4.1%
s 58
3.6%
A 47
2.9%
Other values (7) 215
13.2%
Common
Value Count Frequency (%)
148
100.0%

Most occurring blocks

Value Count Frequency (%)
ASCII 1772
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
e 418
23.6%
r 275
15.5%
g 193
10.9%
148
8.4%
a 121
6.8%
i 99
5.6%
n 66
3.7%
t 66
3.7%
o 66
3.7%
s 58
3.3%
Other values (8) 262
14.8%

B03[6]
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Please indicate whether you agree or disagree with these statements. [Badges based on the acceptance of answers should be used more]

Distinct 5
Distinct (%) 3.9%
Missing 11
Missing (%) 8.0%
Memory size 1.2 KiB
Agree
54
Neither agree nor disagree
45
Strongly agree
17
Disagree
9
Strongly disagree
2

Length

Max length 26
Median length 17
Mean length 14.04724409
Min length 5

Characters and Unicode

Total characters 1784
Distinct characters 18
Distinct categories 3 ?
Distinct scripts 2 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row Strongly agree
2nd row Strongly agree
3rd row Agree
4th row Neither agree nor disagree
5th row Agree

Common Values

Value Count Frequency (%)
Agree 54
39.1%
Neither agree nor disagree 45
32.6%
Strongly agree 17
12.3%
Disagree 9
6.5%
Strongly disagree 2
1.4%
(Missing) 11
8.0%

Length

2022-07-04T20:23:55.771856 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:23:56.016186 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
agree 116
41.3%
disagree 56
19.9%
neither 45
16.0%
nor 45
16.0%
strongly 19
6.8%

Most occurring characters

Value Count Frequency (%)
e 434
24.3%
r 281
15.8%
g 191
10.7%
154
8.6%
a 118
6.6%
i 101
5.7%
n 64
3.6%
t 64
3.6%
o 64
3.6%
s 56
3.1%
Other values (8) 257
14.4%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 1503
84.2%
Space Separator 154
8.6%
Uppercase Letter 127
7.1%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
e 434
28.9%
r 281
18.7%
g 191
12.7%
a 118
7.9%
i 101
6.7%
n 64
4.3%
t 64
4.3%
o 64
4.3%
s 56
3.7%
d 47
3.1%
Other values (3) 83
5.5%
Uppercase Letter
Value Count Frequency (%)
A 54
42.5%
N 45
35.4%
S 19
15.0%
D 9
7.1%
Space Separator
Value Count Frequency (%)
154
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 1630
91.4%
Common 154
8.6%

Most frequent character per script

Latin
Value Count Frequency (%)
e 434
26.6%
r 281
17.2%
g 191
11.7%
a 118
7.2%
i 101
6.2%
n 64
3.9%
t 64
3.9%
o 64
3.9%
s 56
3.4%
A 54
3.3%
Other values (7) 203
12.5%
Common
Value Count Frequency (%)
154
100.0%

Most occurring blocks

Value Count Frequency (%)
ASCII 1784
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
e 434
24.3%
r 281
15.8%
g 191
10.7%
154
8.6%
a 118
6.6%
i 101
5.7%
n 64
3.6%
t 64
3.6%
o 64
3.6%
s 56
3.1%
Other values (8) 257
14.4%

B03[7]
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Please indicate whether you agree or disagree with these statements. [Having a badge for long answers was motivating ]

Distinct 5
Distinct (%) 3.9%
Missing 11
Missing (%) 8.0%
Memory size 1.2 KiB
Agree
42
Neither agree nor disagree
40
Strongly agree
33
Disagree
8
Strongly disagree
4

Length

Max length 26
Median length 17
Mean length 14.51968504
Min length 5

Characters and Unicode

Total characters 1844
Distinct characters 18
Distinct categories 3 ?
Distinct scripts 2 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row Strongly agree
2nd row Strongly agree
3rd row Agree
4th row Strongly agree
5th row Strongly agree

Common Values

Value Count Frequency (%)
Agree 42
30.4%
Neither agree nor disagree 40
29.0%
Strongly agree 33
23.9%
Disagree 8
5.8%
Strongly disagree 4
2.9%
(Missing) 11
8.0%

Length

2022-07-04T20:23:56.249807 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:23:56.492594 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
agree 115
40.5%
disagree 52
18.3%
neither 40
14.1%
nor 40
14.1%
strongly 37
13.0%

Most occurring characters

Value Count Frequency (%)
e 414
22.5%
r 284
15.4%
g 204
11.1%
157
8.5%
a 125
6.8%
i 92
5.0%
o 77
4.2%
t 77
4.2%
n 77
4.2%
s 52
2.8%
Other values (8) 285
15.5%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 1560
84.6%
Space Separator 157
8.5%
Uppercase Letter 127
6.9%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
e 414
26.5%
r 284
18.2%
g 204
13.1%
a 125
8.0%
i 92
5.9%
o 77
4.9%
t 77
4.9%
n 77
4.9%
s 52
3.3%
d 44
2.8%
Other values (3) 114
7.3%
Uppercase Letter
Value Count Frequency (%)
A 42
33.1%
N 40
31.5%
S 37
29.1%
D 8
6.3%
Space Separator
Value Count Frequency (%)
157
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 1687
91.5%
Common 157
8.5%

Most frequent character per script

Latin
Value Count Frequency (%)
e 414
24.5%
r 284
16.8%
g 204
12.1%
a 125
7.4%
i 92
5.5%
o 77
4.6%
t 77
4.6%
n 77
4.6%
s 52
3.1%
d 44
2.6%
Other values (7) 241
14.3%
Common
Value Count Frequency (%)
157
100.0%

Most occurring blocks

Value Count Frequency (%)
ASCII 1844
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
e 414
22.5%
r 284
15.4%
g 204
11.1%
157
8.5%
a 125
6.8%
i 92
5.0%
o 77
4.2%
t 77
4.2%
n 77
4.2%
s 52
2.8%
Other values (8) 285
15.5%

M01
Unsupported

MISSING
REJECTED
UNSUPPORTED

In addition to badges, the chatbot sends messages on occasions. To help us improve messages in future versions please answer the following questions. Here is an example message: “You haven't asked a question yet. You can get help from the community with your questions. Type /question to ask the community!”

Missing 138
Missing (%) 100.0%
Memory size 1.2 KiB

M03[1]
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Please indicate whether you agree or disagree with these statements. [I liked the chatbot’s messages]

Distinct 5
Distinct (%) 3.9%
Missing 11
Missing (%) 8.0%
Memory size 1.2 KiB
Agree
55
Neither agree nor disagree
38
Strongly agree
18
Disagree
15
Strongly disagree
1

Length

Max length 26
Median length 17
Mean length 13.00787402
Min length 5

Characters and Unicode

Total characters 1652
Distinct characters 18
Distinct categories 3 ?
Distinct scripts 2 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 1 ?
Unique (%) 0.8%

Sample

1st row Strongly agree
2nd row Agree
3rd row Agree
4th row Strongly disagree
5th row Agree

Common Values

Value Count Frequency (%)
Agree 55
39.9%
Neither agree nor disagree 38
27.5%
Strongly agree 18
13.0%
Disagree 15
10.9%
Strongly disagree 1
0.7%
(Missing) 11
8.0%

Length

2022-07-04T20:23:56.731789 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:23:56.978794 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
agree 111
42.7%
disagree 54
20.8%
neither 38
14.6%
nor 38
14.6%
strongly 19
7.3%

Most occurring characters

Value Count Frequency (%)
e 406
24.6%
r 260
15.7%
g 184
11.1%
133
8.1%
a 110
6.7%
i 92
5.6%
o 57
3.5%
t 57
3.5%
n 57
3.5%
A 55
3.3%
Other values (8) 241
14.6%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 1392
84.3%
Space Separator 133
8.1%
Uppercase Letter 127
7.7%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
e 406
29.2%
r 260
18.7%
g 184
13.2%
a 110
7.9%
i 92
6.6%
o 57
4.1%
t 57
4.1%
n 57
4.1%
s 54
3.9%
d 39
2.8%
Other values (3) 76
5.5%
Uppercase Letter
Value Count Frequency (%)
A 55
43.3%
N 38
29.9%
S 19
15.0%
D 15
11.8%
Space Separator
Value Count Frequency (%)
133
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 1519
91.9%
Common 133
8.1%

Most frequent character per script

Latin
Value Count Frequency (%)
e 406
26.7%
r 260
17.1%
g 184
12.1%
a 110
7.2%
i 92
6.1%
o 57
3.8%
t 57
3.8%
n 57
3.8%
A 55
3.6%
s 54
3.6%
Other values (7) 187
12.3%
Common
Value Count Frequency (%)
133
100.0%

Most occurring blocks

Value Count Frequency (%)
ASCII 1652
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
e 406
24.6%
r 260
15.7%
g 184
11.1%
133
8.1%
a 110
6.7%
i 92
5.6%
o 57
3.5%
t 57
3.5%
n 57
3.5%
A 55
3.3%
Other values (8) 241
14.6%

M03[2]
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Please indicate whether you agree or disagree with these statements. [The messages were a distraction]

Distinct 5
Distinct (%) 3.9%
Missing 11
Missing (%) 8.0%
Memory size 1.2 KiB
Disagree
49
Neither agree nor disagree
34
Agree
31
Strongly disagree
9
Strongly agree
4

Length

Max length 26
Median length 17
Mean length 12.91338583
Min length 5

Characters and Unicode

Total characters 1640
Distinct characters 18
Distinct categories 3 ?
Distinct scripts 2 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row Strongly disagree
2nd row Disagree
3rd row Disagree
4th row Agree
5th row Disagree

Common Values

Value Count Frequency (%)
Disagree 49
35.5%
Neither agree nor disagree 34
24.6%
Agree 31
22.5%
Strongly disagree 9
6.5%
Strongly agree 4
2.9%
(Missing) 11
8.0%

Length

2022-07-04T20:23:57.469130 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:23:57.723784 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
disagree 92
38.0%
agree 69
28.5%
neither 34
14.0%
nor 34
14.0%
strongly 13
5.4%

Most occurring characters

Value Count Frequency (%)
e 390
23.8%
r 242
14.8%
g 174
10.6%
a 130
7.9%
i 126
7.7%
115
7.0%
s 92
5.6%
D 49
3.0%
o 47
2.9%
t 47
2.9%
Other values (8) 228
13.9%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 1398
85.2%
Uppercase Letter 127
7.7%
Space Separator 115
7.0%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
e 390
27.9%
r 242
17.3%
g 174
12.4%
a 130
9.3%
i 126
9.0%
s 92
6.6%
o 47
3.4%
t 47
3.4%
n 47
3.4%
d 43
3.1%
Other values (3) 60
4.3%
Uppercase Letter
Value Count Frequency (%)
D 49
38.6%
N 34
26.8%
A 31
24.4%
S 13
10.2%
Space Separator
Value Count Frequency (%)
115
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 1525
93.0%
Common 115
7.0%

Most frequent character per script

Latin
Value Count Frequency (%)
e 390
25.6%
r 242
15.9%
g 174
11.4%
a 130
8.5%
i 126
8.3%
s 92
6.0%
D 49
3.2%
o 47
3.1%
t 47
3.1%
n 47
3.1%
Other values (7) 181
11.9%
Common
Value Count Frequency (%)
115
100.0%

Most occurring blocks

Value Count Frequency (%)
ASCII 1640
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
e 390
23.8%
r 242
14.8%
g 174
10.6%
a 130
7.9%
i 126
7.7%
115
7.0%
s 92
5.6%
D 49
3.0%
o 47
2.9%
t 47
2.9%
Other values (8) 228
13.9%

M03[3]
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Please indicate whether you agree or disagree with these statements. [The messages encouraged me to contribute to chatbot]

Distinct 4
Distinct (%) 3.1%
Missing 11
Missing (%) 8.0%
Memory size 1.2 KiB
Agree
63
Neither agree nor disagree
29
Disagree
18
Strongly agree
17

Length

Max length 26
Median length 14
Mean length 11.42519685
Min length 5

Characters and Unicode

Total characters 1451
Distinct characters 18
Distinct categories 3 ?
Distinct scripts 2 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row Strongly agree
2nd row Strongly agree
3rd row Agree
4th row Disagree
5th row Agree

Common Values

Value Count Frequency (%)
Agree 63
45.7%
Neither agree nor disagree 29
21.0%
Disagree 18
13.0%
Strongly agree 17
12.3%
(Missing) 11
8.0%

Length

2022-07-04T20:23:57.972581 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:23:58.222580 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
agree 109
47.2%
disagree 47
20.3%
neither 29
12.6%
nor 29
12.6%
strongly 17
7.4%

Most occurring characters

Value Count Frequency (%)
e 370
25.5%
r 231
15.9%
g 173
11.9%
104
7.2%
a 93
6.4%
i 76
5.2%
A 63
4.3%
s 47
3.2%
t 46
3.2%
n 46
3.2%
Other values (8) 202
13.9%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 1220
84.1%
Uppercase Letter 127
8.8%
Space Separator 104
7.2%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
e 370
30.3%
r 231
18.9%
g 173
14.2%
a 93
7.6%
i 76
6.2%
s 47
3.9%
t 46
3.8%
n 46
3.8%
o 46
3.8%
h 29
2.4%
Other values (3) 63
5.2%
Uppercase Letter
Value Count Frequency (%)
A 63
49.6%
N 29
22.8%
D 18
14.2%
S 17
13.4%
Space Separator
Value Count Frequency (%)
104
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 1347
92.8%
Common 104
7.2%

Most frequent character per script

Latin
Value Count Frequency (%)
e 370
27.5%
r 231
17.1%
g 173
12.8%
a 93
6.9%
i 76
5.6%
A 63
4.7%
s 47
3.5%
t 46
3.4%
n 46
3.4%
o 46
3.4%
Other values (7) 156
11.6%
Common
Value Count Frequency (%)
104
100.0%

Most occurring blocks

Value Count Frequency (%)
ASCII 1451
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
e 370
25.5%
r 231
15.9%
g 173
11.9%
104
7.2%
a 93
6.4%
i 76
5.2%
A 63
4.3%
s 47
3.2%
t 46
3.2%
n 46
3.2%
Other values (8) 202
13.9%

M03[4]
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Please indicate whether you agree or disagree with these statements. [More types of messages should be used]

Distinct 5
Distinct (%) 3.9%
Missing 11
Missing (%) 8.0%
Memory size 1.2 KiB
Agree
45
Neither agree nor disagree
36
Disagree
29
Strongly agree
15
Strongly disagree
2

Length

Max length 26
Median length 17
Mean length 12.88976378
Min length 5

Characters and Unicode

Total characters 1637
Distinct characters 18
Distinct categories 3 ?
Distinct scripts 2 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row Strongly agree
2nd row Strongly agree
3rd row Agree
4th row Disagree
5th row Agree

Common Values

Value Count Frequency (%)
Agree 45
32.6%
Neither agree nor disagree 36
26.1%
Disagree 29
21.0%
Strongly agree 15
10.9%
Strongly disagree 2
1.4%
(Missing) 11
8.0%

Length

2022-07-04T20:23:58.465656 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:23:58.723413 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
agree 96
38.1%
disagree 67
26.6%
neither 36
14.3%
nor 36
14.3%
strongly 17
6.7%

Most occurring characters

Value Count Frequency (%)
e 398
24.3%
r 252
15.4%
g 180
11.0%
125
7.6%
a 118
7.2%
i 103
6.3%
s 67
4.1%
n 53
3.2%
t 53
3.2%
o 53
3.2%
Other values (8) 235
14.4%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 1385
84.6%
Uppercase Letter 127
7.8%
Space Separator 125
7.6%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
e 398
28.7%
r 252
18.2%
g 180
13.0%
a 118
8.5%
i 103
7.4%
s 67
4.8%
n 53
3.8%
t 53
3.8%
o 53
3.8%
d 38
2.7%
Other values (3) 70
5.1%
Uppercase Letter
Value Count Frequency (%)
A 45
35.4%
N 36
28.3%
D 29
22.8%
S 17
13.4%
Space Separator
Value Count Frequency (%)
125
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 1512
92.4%
Common 125
7.6%

Most frequent character per script

Latin
Value Count Frequency (%)
e 398
26.3%
r 252
16.7%
g 180
11.9%
a 118
7.8%
i 103
6.8%
s 67
4.4%
n 53
3.5%
t 53
3.5%
o 53
3.5%
A 45
3.0%
Other values (7) 190
12.6%
Common
Value Count Frequency (%)
125
100.0%

Most occurring blocks

Value Count Frequency (%)
ASCII 1637
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
e 398
24.3%
r 252
15.4%
g 180
11.0%
125
7.6%
a 118
7.2%
i 103
6.3%
s 67
4.1%
n 53
3.2%
t 53
3.2%
o 53
3.2%
Other values (8) 235
14.4%

M03[5]
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Please indicate whether you agree or disagree with these statements. [Messages should be sent less frequently]

Distinct 5
Distinct (%) 3.9%
Missing 11
Missing (%) 8.0%
Memory size 1.2 KiB
Neither agree nor disagree
48
Agree
41
Disagree
25
Strongly agree
9
Strongly disagree
4

Length

Max length 26
Median length 17
Mean length 14.54330709
Min length 5

Characters and Unicode

Total characters 1847
Distinct characters 18
Distinct categories 3 ?
Distinct scripts 2 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row Strongly agree
2nd row Strongly agree
3rd row Neither agree nor disagree
4th row Agree
5th row Disagree

Common Values

Value Count Frequency (%)
Neither agree nor disagree 48
34.8%
Agree 41
29.7%
Disagree 25
18.1%
Strongly agree 9
6.5%
Strongly disagree 4
2.9%
(Missing) 11
8.0%

Length

2022-07-04T20:23:58.966480 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:23:59.207193 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
agree 98
34.5%
disagree 77
27.1%
neither 48
16.9%
nor 48
16.9%
strongly 13
4.6%

Most occurring characters

Value Count Frequency (%)
e 446
24.1%
r 284
15.4%
g 188
10.2%
157
8.5%
a 134
7.3%
i 125
6.8%
s 77
4.2%
n 61
3.3%
t 61
3.3%
o 61
3.3%
Other values (8) 253
13.7%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 1563
84.6%
Space Separator 157
8.5%
Uppercase Letter 127
6.9%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
e 446
28.5%
r 284
18.2%
g 188
12.0%
a 134
8.6%
i 125
8.0%
s 77
4.9%
n 61
3.9%
t 61
3.9%
o 61
3.9%
d 52
3.3%
Other values (3) 74
4.7%
Uppercase Letter
Value Count Frequency (%)
N 48
37.8%
A 41
32.3%
D 25
19.7%
S 13
10.2%
Space Separator
Value Count Frequency (%)
157
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 1690
91.5%
Common 157
8.5%

Most frequent character per script

Latin
Value Count Frequency (%)
e 446
26.4%
r 284
16.8%
g 188
11.1%
a 134
7.9%
i 125
7.4%
s 77
4.6%
n 61
3.6%
t 61
3.6%
o 61
3.6%
d 52
3.1%
Other values (7) 201
11.9%
Common
Value Count Frequency (%)
157
100.0%

Most occurring blocks

Value Count Frequency (%)
ASCII 1847
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
e 446
24.1%
r 284
15.4%
g 188
10.2%
157
8.5%
a 134
7.3%
i 125
6.8%
s 77
4.2%
n 61
3.3%
t 61
3.3%
o 61
3.3%
Other values (8) 253
13.7%

M03[6]
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Please indicate whether you agree or disagree with these statements. [Messages should be personalised for each user]

Distinct 5
Distinct (%) 3.9%
Missing 11
Missing (%) 8.0%
Memory size 1.2 KiB
Agree
49
Neither agree nor disagree
45
Disagree
16
Strongly agree
13
Strongly disagree
4

Length

Max length 26
Median length 17
Mean length 14.11811024
Min length 5

Characters and Unicode

Total characters 1793
Distinct characters 18
Distinct categories 3 ?
Distinct scripts 2 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row Strongly agree
2nd row Strongly disagree
3rd row Agree
4th row Neither agree nor disagree
5th row Disagree

Common Values

Value Count Frequency (%)
Agree 49
35.5%
Neither agree nor disagree 45
32.6%
Disagree 16
11.6%
Strongly agree 13
9.4%
Strongly disagree 4
2.9%
(Missing) 11
8.0%

Length

2022-07-04T20:23:59.445248 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:23:59.703428 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
agree 107
38.4%
disagree 65
23.3%
neither 45
16.1%
nor 45
16.1%
strongly 17
6.1%

Most occurring characters

Value Count Frequency (%)
e 434
24.2%
r 279
15.6%
g 189
10.5%
152
8.5%
a 123
6.9%
i 110
6.1%
s 65
3.6%
o 62
3.5%
t 62
3.5%
n 62
3.5%
Other values (8) 255
14.2%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 1514
84.4%
Space Separator 152
8.5%
Uppercase Letter 127
7.1%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
e 434
28.7%
r 279
18.4%
g 189
12.5%
a 123
8.1%
i 110
7.3%
s 65
4.3%
o 62
4.1%
t 62
4.1%
n 62
4.1%
d 49
3.2%
Other values (3) 79
5.2%
Uppercase Letter
Value Count Frequency (%)
A 49
38.6%
N 45
35.4%
S 17
13.4%
D 16
12.6%
Space Separator
Value Count Frequency (%)
152
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 1641
91.5%
Common 152
8.5%

Most frequent character per script

Latin
Value Count Frequency (%)
e 434
26.4%
r 279
17.0%
g 189
11.5%
a 123
7.5%
i 110
6.7%
s 65
4.0%
o 62
3.8%
t 62
3.8%
n 62
3.8%
d 49
3.0%
Other values (7) 206
12.6%
Common
Value Count Frequency (%)
152
100.0%

Most occurring blocks

Value Count Frequency (%)
ASCII 1793
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
e 434
24.2%
r 279
15.6%
g 189
10.5%
152
8.5%
a 123
6.9%
i 110
6.1%
s 65
3.6%
o 62
3.5%
t 62
3.5%
n 62
3.5%
Other values (8) 255
14.2%

M03[7]
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Please indicate whether you agree or disagree with these statements. [I liked the message: “Help the community with answering questions or ask a new question!"]

Distinct 4
Distinct (%) 3.1%
Missing 11
Missing (%) 8.0%
Memory size 1.2 KiB
Agree
58
Neither agree nor disagree
34
Strongly agree
21
Disagree
14

Length

Max length 26
Median length 14
Mean length 12.44094488
Min length 5

Characters and Unicode

Total characters 1580
Distinct characters 18
Distinct categories 3 ?
Distinct scripts 2 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row Strongly agree
2nd row Strongly agree
3rd row Neither agree nor disagree
4th row Neither agree nor disagree
5th row Agree

Common Values

Value Count Frequency (%)
Agree 58
42.0%
Neither agree nor disagree 34
24.6%
Strongly agree 21
15.2%
Disagree 14
10.1%
(Missing) 11
8.0%

Length

2022-07-04T20:23:59.947365 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:24:00.188074 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
agree 113
45.2%
disagree 48
19.2%
neither 34
13.6%
nor 34
13.6%
strongly 21
8.4%

Most occurring characters

Value Count Frequency (%)
e 390
24.7%
r 250
15.8%
g 182
11.5%
123
7.8%
a 103
6.5%
i 82
5.2%
A 58
3.7%
t 55
3.5%
n 55
3.5%
o 55
3.5%
Other values (8) 227
14.4%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 1330
84.2%
Uppercase Letter 127
8.0%
Space Separator 123
7.8%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
e 390
29.3%
r 250
18.8%
g 182
13.7%
a 103
7.7%
i 82
6.2%
t 55
4.1%
n 55
4.1%
o 55
4.1%
s 48
3.6%
h 34
2.6%
Other values (3) 76
5.7%
Uppercase Letter
Value Count Frequency (%)
A 58
45.7%
N 34
26.8%
S 21
16.5%
D 14
11.0%
Space Separator
Value Count Frequency (%)
123
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 1457
92.2%
Common 123
7.8%

Most frequent character per script

Latin
Value Count Frequency (%)
e 390
26.8%
r 250
17.2%
g 182
12.5%
a 103
7.1%
i 82
5.6%
A 58
4.0%
t 55
3.8%
n 55
3.8%
o 55
3.8%
s 48
3.3%
Other values (7) 179
12.3%
Common
Value Count Frequency (%)
123
100.0%

Most occurring blocks

Value Count Frequency (%)
ASCII 1580
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
e 390
24.7%
r 250
15.8%
g 182
11.5%
123
7.8%
a 103
6.5%
i 82
5.2%
A 58
3.7%
t 55
3.5%
n 55
3.5%
o 55
3.5%
Other values (8) 227
14.4%

A[A1]
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Over the last year or so, how often have you done the following? [I have helped carry a stranger’s belongings]

Distinct 4
Distinct (%) 3.1%
Missing 11
Missing (%) 8.0%
Memory size 1.2 KiB
Sometimes
50
Never
40
Rarely
29
Frequently
8

Length

Max length 10
Median length 9
Mean length 7.118110236
Min length 5

Characters and Unicode

Total characters 904
Distinct characters 18
Distinct categories 2 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row Sometimes
2nd row Sometimes
3rd row Sometimes
4th row Sometimes
5th row Frequently

Common Values

Value Count Frequency (%)
Sometimes 50
36.2%
Never 40
29.0%
Rarely 29
21.0%
Frequently 8
5.8%
(Missing) 11
8.0%

Length

2022-07-04T20:24:00.425736 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:24:00.666981 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
sometimes 50
39.4%
never 40
31.5%
rarely 29
22.8%
frequently 8
6.3%

Most occurring characters

Value Count Frequency (%)
e 225
24.9%
m 100
11.1%
r 77
8.5%
t 58
6.4%
s 50
5.5%
o 50
5.5%
S 50
5.5%
i 50
5.5%
N 40
4.4%
v 40
4.4%
Other values (8) 164
18.1%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 777
86.0%
Uppercase Letter 127
14.0%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
e 225
29.0%
m 100
12.9%
r 77
9.9%
t 58
7.5%
s 50
6.4%
o 50
6.4%
i 50
6.4%
v 40
5.1%
l 37
4.8%
y 37
4.8%
Other values (4) 53
6.8%
Uppercase Letter
Value Count Frequency (%)
S 50
39.4%
N 40
31.5%
R 29
22.8%
F 8
6.3%

Most occurring scripts

Value Count Frequency (%)
Latin 904
100.0%

Most frequent character per script

Latin
Value Count Frequency (%)
e 225
24.9%
m 100
11.1%
r 77
8.5%
t 58
6.4%
s 50
5.5%
o 50
5.5%
S 50
5.5%
i 50
5.5%
N 40
4.4%
v 40
4.4%
Other values (8) 164
18.1%

Most occurring blocks

Value Count Frequency (%)
ASCII 904
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
e 225
24.9%
m 100
11.1%
r 77
8.5%
t 58
6.4%
s 50
5.5%
o 50
5.5%
S 50
5.5%
i 50
5.5%
N 40
4.4%
v 40
4.4%
Other values (8) 164
18.1%

A[A2]
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Over the last year or so, how often have you done the following? [I have exchanged a note for small change for a stranger]

Distinct 4
Distinct (%) 3.1%
Missing 11
Missing (%) 8.0%
Memory size 1.2 KiB
Never
68
Sometimes
27
Rarely
27
Frequently
5

Length

Max length 10
Median length 5
Mean length 6.25984252
Min length 5

Characters and Unicode

Total characters 795
Distinct characters 18
Distinct categories 2 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row Never
2nd row Frequently
3rd row Sometimes
4th row Rarely
5th row Sometimes

Common Values

Value Count Frequency (%)
Never 68
49.3%
Sometimes 27
19.6%
Rarely 27
19.6%
Frequently 5
3.6%
(Missing) 11
8.0%

Length

2022-07-04T20:24:00.896751 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:24:01.138106 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
never 68
53.5%
sometimes 27
21.3%
rarely 27
21.3%
frequently 5
3.9%

Most occurring characters

Value Count Frequency (%)
e 227
28.6%
r 100
12.6%
N 68
8.6%
v 68
8.6%
m 54
6.8%
y 32
4.0%
l 32
4.0%
t 32
4.0%
R 27
3.4%
a 27
3.4%
Other values (8) 128
16.1%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 668
84.0%
Uppercase Letter 127
16.0%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
e 227
34.0%
r 100
15.0%
v 68
10.2%
m 54
8.1%
y 32
4.8%
l 32
4.8%
t 32
4.8%
a 27
4.0%
s 27
4.0%
i 27
4.0%
Other values (4) 42
6.3%
Uppercase Letter
Value Count Frequency (%)
N 68
53.5%
R 27
21.3%
S 27
21.3%
F 5
3.9%

Most occurring scripts

Value Count Frequency (%)
Latin 795
100.0%

Most frequent character per script

Latin
Value Count Frequency (%)
e 227
28.6%
r 100
12.6%
N 68
8.6%
v 68
8.6%
m 54
6.8%
y 32
4.0%
l 32
4.0%
t 32
4.0%
R 27
3.4%
a 27
3.4%
Other values (8) 128
16.1%

Most occurring blocks

Value Count Frequency (%)
ASCII 795
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
e 227
28.6%
r 100
12.6%
N 68
8.6%
v 68
8.6%
m 54
6.8%
y 32
4.0%
l 32
4.0%
t 32
4.0%
R 27
3.4%
a 27
3.4%
Other values (8) 128
16.1%

A[A3]
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Over the last year or so, how often have you done the following? [I have helped an acquaintance to move houses ]

Distinct 4
Distinct (%) 3.1%
Missing 11
Missing (%) 8.0%
Memory size 1.2 KiB
Never
53
Sometimes
36
Rarely
29
Frequently
9

Length

Max length 10
Median length 9
Mean length 6.716535433
Min length 5

Characters and Unicode

Total characters 853
Distinct characters 18
Distinct categories 2 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row Rarely
2nd row Never
3rd row Never
4th row Never
5th row Sometimes

Common Values

Value Count Frequency (%)
Never 53
38.4%
Sometimes 36
26.1%
Rarely 29
21.0%
Frequently 9
6.5%
(Missing) 11
8.0%

Length

2022-07-04T20:24:01.367378 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:24:01.620590 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
never 53
41.7%
sometimes 36
28.3%
rarely 29
22.8%
frequently 9
7.1%

Most occurring characters

Value Count Frequency (%)
e 225
26.4%
r 91
10.7%
m 72
8.4%
N 53
6.2%
v 53
6.2%
t 45
5.3%
y 38
4.5%
l 38
4.5%
s 36
4.2%
i 36
4.2%
Other values (8) 166
19.5%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 726
85.1%
Uppercase Letter 127
14.9%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
e 225
31.0%
r 91
12.5%
m 72
9.9%
v 53
7.3%
t 45
6.2%
y 38
5.2%
l 38
5.2%
s 36
5.0%
i 36
5.0%
o 36
5.0%
Other values (4) 56
7.7%
Uppercase Letter
Value Count Frequency (%)
N 53
41.7%
S 36
28.3%
R 29
22.8%
F 9
7.1%

Most occurring scripts

Value Count Frequency (%)
Latin 853
100.0%

Most frequent character per script

Latin
Value Count Frequency (%)
e 225
26.4%
r 91
10.7%
m 72
8.4%
N 53
6.2%
v 53
6.2%
t 45
5.3%
y 38
4.5%
l 38
4.5%
s 36
4.2%
i 36
4.2%
Other values (8) 166
19.5%

Most occurring blocks

Value Count Frequency (%)
ASCII 853
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
e 225
26.4%
r 91
10.7%
m 72
8.4%
N 53
6.2%
v 53
6.2%
t 45
5.3%
y 38
4.5%
l 38
4.5%
s 36
4.2%
i 36
4.2%
Other values (8) 166
19.5%

A[A4]
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Over the last year or so, how often have you done the following? [I have let a neighbour I did not know well borrow an item of some value to me ]

Distinct 4
Distinct (%) 3.1%
Missing 11
Missing (%) 8.0%
Memory size 1.2 KiB
Never
68
Rarely
28
Sometimes
21
Frequently
10

Length

Max length 10
Median length 5
Mean length 6.275590551
Min length 5

Characters and Unicode

Total characters 797
Distinct characters 18
Distinct categories 2 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row Never
2nd row Never
3rd row Never
4th row Never
5th row Frequently

Common Values

Value Count Frequency (%)
Never 68
49.3%
Rarely 28
20.3%
Sometimes 21
15.2%
Frequently 10
7.2%
(Missing) 11
8.0%

Length

2022-07-04T20:24:01.848160 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:24:02.093593 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
never 68
53.5%
rarely 28
22.0%
sometimes 21
16.5%
frequently 10
7.9%

Most occurring characters

Value Count Frequency (%)
e 226
28.4%
r 106
13.3%
N 68
8.5%
v 68
8.5%
m 42
5.3%
l 38
4.8%
y 38
4.8%
t 31
3.9%
R 28
3.5%
a 28
3.5%
Other values (8) 124
15.6%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 670
84.1%
Uppercase Letter 127
15.9%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
e 226
33.7%
r 106
15.8%
v 68
10.1%
m 42
6.3%
l 38
5.7%
y 38
5.7%
t 31
4.6%
a 28
4.2%
i 21
3.1%
s 21
3.1%
Other values (4) 51
7.6%
Uppercase Letter
Value Count Frequency (%)
N 68
53.5%
R 28
22.0%
S 21
16.5%
F 10
7.9%

Most occurring scripts

Value Count Frequency (%)
Latin 797
100.0%

Most frequent character per script

Latin
Value Count Frequency (%)
e 226
28.4%
r 106
13.3%
N 68
8.5%
v 68
8.5%
m 42
5.3%
l 38
4.8%
y 38
4.8%
t 31
3.9%
R 28
3.5%
a 28
3.5%
Other values (8) 124
15.6%

Most occurring blocks

Value Count Frequency (%)
ASCII 797
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
e 226
28.4%
r 106
13.3%
N 68
8.5%
v 68
8.5%
m 42
5.3%
l 38
4.8%
y 38
4.8%
t 31
3.9%
R 28
3.5%
a 28
3.5%
Other values (8) 124
15.6%

A[A5]
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Over the last year or so, how often have you done the following? [I have offered to help a disabled or elderly stranger across a street ]

Distinct 4
Distinct (%) 3.1%
Missing 11
Missing (%) 8.0%
Memory size 1.2 KiB
Never
46
Sometimes
34
Rarely
31
Frequently
16

Length

Max length 10
Median length 9
Mean length 6.94488189
Min length 5

Characters and Unicode

Total characters 882
Distinct characters 18
Distinct categories 2 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row Frequently
2nd row Rarely
3rd row Never
4th row Sometimes
5th row Frequently

Common Values

Value Count Frequency (%)
Never 46
33.3%
Sometimes 34
24.6%
Rarely 31
22.5%
Frequently 16
11.6%
(Missing) 11
8.0%

Length

2022-07-04T20:24:02.326623 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:24:02.577916 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
never 46
36.2%
sometimes 34
26.8%
rarely 31
24.4%
frequently 16
12.6%

Most occurring characters

Value Count Frequency (%)
e 223
25.3%
r 93
10.5%
m 68
7.7%
t 50
5.7%
y 47
5.3%
l 47
5.3%
N 46
5.2%
v 46
5.2%
s 34
3.9%
i 34
3.9%
Other values (8) 194
22.0%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 755
85.6%
Uppercase Letter 127
14.4%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
e 223
29.5%
r 93
12.3%
m 68
9.0%
t 50
6.6%
y 47
6.2%
l 47
6.2%
v 46
6.1%
s 34
4.5%
i 34
4.5%
o 34
4.5%
Other values (4) 79
10.5%
Uppercase Letter
Value Count Frequency (%)
N 46
36.2%
S 34
26.8%
R 31
24.4%
F 16
12.6%

Most occurring scripts

Value Count Frequency (%)
Latin 882
100.0%

Most frequent character per script

Latin
Value Count Frequency (%)
e 223
25.3%
r 93
10.5%
m 68
7.7%
t 50
5.7%
y 47
5.3%
l 47
5.3%
N 46
5.2%
v 46
5.2%
s 34
3.9%
i 34
3.9%
Other values (8) 194
22.0%

Most occurring blocks

Value Count Frequency (%)
ASCII 882
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
e 223
25.3%
r 93
10.5%
m 68
7.7%
t 50
5.7%
y 47
5.3%
l 47
5.3%
N 46
5.2%
v 46
5.2%
s 34
3.9%
i 34
3.9%
Other values (8) 194
22.0%

A[A6]
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Over the last year or so, how often have you done the following? [I have offered my seat to a pregnant person who was standing]

Distinct 4
Distinct (%) 3.1%
Missing 11
Missing (%) 8.0%
Memory size 1.2 KiB
Sometimes
50
Frequently
39
Never
26
Rarely
12

Length

Max length 10
Median length 9
Mean length 8.204724409
Min length 5

Characters and Unicode

Total characters 1042
Distinct characters 18
Distinct categories 2 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row Frequently
2nd row Sometimes
3rd row Frequently
4th row Frequently
5th row Sometimes

Common Values

Value Count Frequency (%)
Sometimes 50
36.2%
Frequently 39
28.3%
Never 26
18.8%
Rarely 12
8.7%
(Missing) 11
8.0%

Length

2022-07-04T20:24:02.803803 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:24:03.044795 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
sometimes 50
39.4%
frequently 39
30.7%
never 26
20.5%
rarely 12
9.4%

Most occurring characters

Value Count Frequency (%)
e 242
23.2%
m 100
9.6%
t 89
8.5%
r 77
7.4%
y 51
4.9%
l 51
4.9%
S 50
4.8%
i 50
4.8%
s 50
4.8%
o 50
4.8%
Other values (8) 232
22.3%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 915
87.8%
Uppercase Letter 127
12.2%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
e 242
26.4%
m 100
10.9%
t 89
9.7%
r 77
8.4%
y 51
5.6%
l 51
5.6%
i 50
5.5%
s 50
5.5%
o 50
5.5%
n 39
4.3%
Other values (4) 116
12.7%
Uppercase Letter
Value Count Frequency (%)
S 50
39.4%
F 39
30.7%
N 26
20.5%
R 12
9.4%

Most occurring scripts

Value Count Frequency (%)
Latin 1042
100.0%

Most frequent character per script

Latin
Value Count Frequency (%)
e 242
23.2%
m 100
9.6%
t 89
8.5%
r 77
7.4%
y 51
4.9%
l 51
4.9%
S 50
4.8%
i 50
4.8%
s 50
4.8%
o 50
4.8%
Other values (8) 232
22.3%

Most occurring blocks

Value Count Frequency (%)
ASCII 1042
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
e 242
23.2%
m 100
9.6%
t 89
8.5%
r 77
7.4%
y 51
4.9%
l 51
4.9%
S 50
4.8%
i 50
4.8%
s 50
4.8%
o 50
4.8%
Other values (8) 232
22.3%

A[A7]
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Over the last year or so, how often have you done the following? [I have spent time helping other students]

Distinct 4
Distinct (%) 3.1%
Missing 11
Missing (%) 8.0%
Memory size 1.2 KiB
Sometimes
59
Frequently
47
Rarely
16
Never
5

Length

Max length 10
Median length 9
Mean length 8.834645669
Min length 5

Characters and Unicode

Total characters 1122
Distinct characters 18
Distinct categories 2 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row Frequently
2nd row Sometimes
3rd row Frequently
4th row Rarely
5th row Frequently

Common Values

Value Count Frequency (%)
Sometimes 59
42.8%
Frequently 47
34.1%
Rarely 16
11.6%
Never 5
3.6%
(Missing) 11
8.0%

Length

2022-07-04T20:24:03.275749 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:24:03.516485 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
sometimes 59
46.5%
frequently 47
37.0%
rarely 16
12.6%
never 5
3.9%

Most occurring characters

Value Count Frequency (%)
e 238
21.2%
m 118
10.5%
t 106
9.4%
r 68
6.1%
y 63
5.6%
l 63
5.6%
S 59
5.3%
i 59
5.3%
s 59
5.3%
o 59
5.3%
Other values (8) 230
20.5%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 995
88.7%
Uppercase Letter 127
11.3%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
e 238
23.9%
m 118
11.9%
t 106
10.7%
r 68
6.8%
y 63
6.3%
l 63
6.3%
i 59
5.9%
s 59
5.9%
o 59
5.9%
n 47
4.7%
Other values (4) 115
11.6%
Uppercase Letter
Value Count Frequency (%)
S 59
46.5%
F 47
37.0%
R 16
12.6%
N 5
3.9%

Most occurring scripts

Value Count Frequency (%)
Latin 1122
100.0%

Most frequent character per script

Latin
Value Count Frequency (%)
e 238
21.2%
m 118
10.5%
t 106
9.4%
r 68
6.1%
y 63
5.6%
l 63
5.6%
S 59
5.3%
i 59
5.3%
s 59
5.3%
o 59
5.3%
Other values (8) 230
20.5%

Most occurring blocks

Value Count Frequency (%)
ASCII 1122
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
e 238
21.2%
m 118
10.5%
t 106
9.4%
r 68
6.1%
y 63
5.6%
l 63
5.6%
S 59
5.3%
i 59
5.3%
s 59
5.3%
o 59
5.3%
Other values (8) 230
20.5%

F01
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Thanks for completing the questionnaire! Please click on the "Submit" button to finalise your answers. If you have any other comments on the chatbot, we would be pleased to read them.

Distinct 44
Distinct (%) 100.0%
Missing 94
Missing (%) 68.1%
Memory size 1.2 KiB
Evaluations of the chat bot’s utility are somewhat hampered by the fact that the overwhelming majority of questions asked were fairly mundane. I don’t see the Chat Bot as a useful tool to get to know people, but rather as a means of getting information on administrative or other issues. As such, it would be good if those questions had been asked rather than “What is your favourite book?” etc.
1
I found the system particularly unfit for the type of questions we asked. Most of the questions asked were open-ended and aimed at collecting various opinions/ recommendations. And yet the vast majority of the time, I only received one answer to my questions. I'm not sure if it's because the chatbot asks the question to one person at a time, if once I accept an answer I cannot receive any more or if I simply only ever got one answer to my question, but the system seems to be made for questions that require help rather than recommendations and tips, which is what we asked most often.
1
I really enjoyed working with We@Net while I could've used it more I see a future for it helping students around campus.
1
El chatbot me parecio mas util para conocer ciertas opiniones que tenian las personas de algun tema en especifico. En ciertos casos tambien es util para buscar recomendaciones de algunas cosas.
1
Muy interesante experiencia! Gracias por la oportunidad.
1
Other values (39)
39

Length

Max length 1207
Median length 142.5
Mean length 238.7727273
Min length 5

Characters and Unicode

Total characters 10506
Distinct characters 113
Distinct categories 10 ?
Distinct scripts 3 ?
Distinct blocks 4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 44 ?
Unique (%) 100.0%

Sample

1st row Сонирхолтой байсан шүү
2nd row загвар дизайн хэрэглэхэд жоохон төвөгтэй санагдсан
3rd row Hariult awchaad tuunde reply hiij zuwhun ter hariultiig ilgeesen hentei bas chatalmaar sanagdsn
4th row Маш идэвхтэй оролцож чадаагүй ч хэсэгхэн хугацаанд хамт олонтой найзтай болсон юм шиг санагдахаар их дотно мэдрэмж төрүүлсэн та бүхэндээ баярлалаа
5th row Ашиглахад илүү амархан энгийн байвал гоё. Асуулт асуух гэхээр баахан шүүлтүүртэй ингэж асуух уу тэгж асуух уу гээд байдаг. Тэрийгээ арай үр дүнтэйг нь авч ашиглаад цөөхөн болгосон нь дээр байх. Хариулт бичиж байхад асуулт ирвэл тасалддаг энийг засах хэрэгтэй. Хариулт таалагдсан гэдэг дээр дарахаар асуултын хариултууд ирэхээ больчихдог. Тэгэхээр олон хариулт авч чаддаггүй. Аан бас шууд дараад илгээдэг бэлэн хариултууд байвал зүгээр байх. Баярлалаа. Өглөөний мэнд. гэх мэт

Common Values

Value Count Frequency (%)
Evaluations of the chat bot’s utility are somewhat hampered by the fact that the overwhelming majority of questions asked were fairly mundane. I don’t see the Chat Bot as a useful tool to get to know people, but rather as a means of getting information on administrative or other issues. As such, it would be good if those questions had been asked rather than “What is your favourite book?” etc. 1
0.7%
I found the system particularly unfit for the type of questions we asked. Most of the questions asked were open-ended and aimed at collecting various opinions/ recommendations. And yet the vast majority of the time, I only received one answer to my questions. I'm not sure if it's because the chatbot asks the question to one person at a time, if once I accept an answer I cannot receive any more or if I simply only ever got one answer to my question, but the system seems to be made for questions that require help rather than recommendations and tips, which is what we asked most often. 1
0.7%
I really enjoyed working with We@Net while I could've used it more I see a future for it helping students around campus. 1
0.7%
El chatbot me parecio mas util para conocer ciertas opiniones que tenian las personas de algun tema en especifico. En ciertos casos tambien es util para buscar recomendaciones de algunas cosas. 1
0.7%
Muy interesante experiencia! Gracias por la oportunidad. 1
0.7%
La experiencia del chatbot me pareció muy buena y útil, mi recomendación sería agrupar de alguna manera todas las respuestas a una pregunta y que sean visibles para los demás usuarios, así podría ayudar a más estudiantes. 1
0.7%
Como general de la experiencia fue linda pero tuve poco retorno a los bugs y errores reportados ya sean desde el bot como por correo, varias veces pase dias enteros sin poder enviar mensajes o conectarme por diversos errores 1
0.7%
Cuando se respondía rápido una pregunta fue muy limitante ya que no permitía más responderlas, el resto genial. 1
0.7%
Thank you for an interesting experience! 1
0.7%
I would have liked to be able to accept answers but still recieve more answers. I would also have liked better and more interactive instructions. The function to ask without your name attached, I just learn about recently by chance. 1
0.7%
Other values (34) 34
24.6%
(Missing) 94
68.1%

Length

2022-07-04T20:24:03.804566 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
Value Count Frequency (%)
the 50
2.9%
to 45
2.6%
i 37
2.1%
a 33
1.9%
it 23
1.3%
questions 18
1.0%
of 15
0.9%
in 15
0.9%
and 14
0.8%
хариулт 14
0.8%
Other values (856) 1476
84.8%

Most occurring characters

Value Count Frequency (%)
1698
16.2%
e 852
8.1%
t 598
5.7%
a 584
5.6%
o 571
5.4%
i 490
4.7%
s 475
4.5%
n 471
4.5%
r 402
3.8%
а 293
2.8%
Other values (103) 4072
38.8%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 8464
80.6%
Space Separator 1698
16.2%
Other Punctuation 187
1.8%
Uppercase Letter 129
1.2%
Control 13
0.1%
Close Punctuation 5
< 0.1%
Open Punctuation 3
< 0.1%
Dash Punctuation 3
< 0.1%
Final Punctuation 3
< 0.1%
Initial Punctuation 1
< 0.1%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
e 852
10.1%
t 598
7.1%
a 584
6.9%
o 571
6.7%
i 490
5.8%
s 475
5.6%
n 471
5.6%
r 402
4.7%
а 293
3.5%
l 261
3.1%
Other values (56) 3467
41.0%
Uppercase Letter
Value Count Frequency (%)
I 47
36.4%
T 12
9.3%
А 7
5.4%
C 7
5.4%
S 5
3.9%
N 5
3.9%
A 4
3.1%
Т 4
3.1%
H 3
2.3%
Х 3
2.3%
Other values (20) 32
24.8%
Other Punctuation
Value Count Frequency (%)
. 84
44.9%
, 65
34.8%
' 21
11.2%
" 6
3.2%
! 4
2.1%
/ 2
1.1%
? 2
1.1%
: 2
1.1%
@ 1
0.5%
Final Punctuation
Value Count Frequency (%)
’ 2
66.7%
” 1
33.3%
Space Separator
Value Count Frequency (%)
1698
100.0%
Control
Value Count Frequency (%)
13
100.0%
Close Punctuation
Value Count Frequency (%)
) 5
100.0%
Open Punctuation
Value Count Frequency (%)
( 3
100.0%
Dash Punctuation
Value Count Frequency (%)
- 3
100.0%
Initial Punctuation
Value Count Frequency (%)
“ 1
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 6781
64.5%
Common 1913
18.2%
Cyrillic 1812
17.2%

Most frequent character per script

Latin
Value Count Frequency (%)
e 852
12.6%
t 598
8.8%
a 584
8.6%
o 571
8.4%
i 490
7.2%
s 475
7.0%
n 471
6.9%
r 402
5.9%
l 261
3.8%
u 252
3.7%
Other values (43) 1825
26.9%
Cyrillic
Value Count Frequency (%)
а 293
16.2%
л 127
7.0%
э 121
6.7%
н 110
6.1%
г 103
5.7%
х 99
5.5%
у 99
5.5%
д 97
5.4%
р 93
5.1%
с 85
4.7%
Other values (33) 585
32.3%
Common
Value Count Frequency (%)
1698
88.8%
. 84
4.4%
, 65
3.4%
' 21
1.1%
13
0.7%
" 6
0.3%
) 5
0.3%
! 4
0.2%
( 3
0.2%
- 3
0.2%
Other values (7) 11
0.6%

Most occurring blocks

Value Count Frequency (%)
ASCII 8661
82.4%
Cyrillic 1812
17.2%
None 29
0.3%
Punctuation 4
< 0.1%

Most frequent character per block

ASCII
Value Count Frequency (%)
1698
19.6%
e 852
9.8%
t 598
6.9%
a 584
6.7%
o 571
6.6%
i 490
5.7%
s 475
5.5%
n 471
5.4%
r 402
4.6%
l 261
3.0%
Other values (48) 2259
26.1%
Cyrillic
Value Count Frequency (%)
а 293
16.2%
л 127
7.0%
э 121
6.7%
н 110
6.1%
г 103
5.7%
х 99
5.5%
у 99
5.5%
д 97
5.4%
р 93
5.1%
с 85
4.7%
Other values (33) 585
32.3%
None
Value Count Frequency (%)
ù 6
20.7%
í 5
17.2%
è 5
17.2%
á 4
13.8%
à 4
13.8%
ó 2
6.9%
é 1
3.4%
ú 1
3.4%
ì 1
3.4%
Punctuation
Value Count Frequency (%)
’ 2
50.0%
“ 1
25.0%
” 1
25.0%

interviewtime
Real number (ℝ ≥0 )

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Total time

Distinct 137
Distinct (%) 99.3%
Missing 0
Missing (%) 0.0%
Infinite 0
Infinite (%) 0.0%
Mean 823.8461594
Minimum 0
Maximum 24096
Zeros 2
Zeros (%) 1.4%
Negative 0
Negative (%) 0.0%
Memory size 1.2 KiB
2022-07-04T20:24:04.347335 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum 0
5-th percentile 52.8275
Q1 235.8125
median 333.795
Q3 489.6375
95-th percentile 2132.402
Maximum 24096
Range 24096
Interquartile range (IQR) 253.825

Descriptive statistics

Standard deviation 2445.130675
Coefficient of variation (CV) 2.96794571
Kurtosis 63.77328469
Mean 823.8461594
Median Absolute Deviation (MAD) 108.925
Skewness 7.450086816
Sum 113690.77
Variance 5978664.016
Monotonicity Not monotonic
2022-07-04T20:24:04.646795 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Value Count Frequency (%)
0 2
1.4%
399.72 1
0.7%
372.77 1
0.7%
305.68 1
0.7%
386 1
0.7%
177.63 1
0.7%
127.56 1
0.7%
236.99 1
0.7%
235.76 1
0.7%
618 1
0.7%
Other values (127) 127
92.0%
Value Count Frequency (%)
0 2
1.4%
5.37 1
0.7%
13.5 1
0.7%
17.08 1
0.7%
18.99 1
0.7%
40.8 1
0.7%
54.95 1
0.7%
74.54 1
0.7%
86.17 1
0.7%
117.47 1
0.7%
Value Count Frequency (%)
24096 1
0.7%
11311.6 1
0.7%
9873.3 1
0.7%
6525.62 1
0.7%
3458.21 1
0.7%
2984.73 1
0.7%
2893.9 1
0.7%
1998.02 1
0.7%
1743.14 1
0.7%
1525.88 1
0.7%

Group time: Onboarding procedures
Real number (ℝ ≥0 )

HIGH CORRELATION
MISSING

Distinct 134
Distinct (%) 98.5%
Missing 2
Missing (%) 1.4%
Infinite 0
Infinite (%) 0.0%
Mean 22.70191176
Minimum 4.71
Maximum 126.49
Zeros 0
Zeros (%) 0.0%
Negative 0
Negative (%) 0.0%
Memory size 1.2 KiB
2022-07-04T20:24:04.940789 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum 4.71
5-th percentile 7.71
Q1 11.755
median 16.01
Q3 23.655
95-th percentile 60.3825
Maximum 126.49
Range 121.78
Interquartile range (IQR) 11.9

Descriptive statistics

Standard deviation 20.53373941
Coefficient of variation (CV) 0.9044938427
Kurtosis 11.62176976
Mean 22.70191176
Median Absolute Deviation (MAD) 5.705
Skewness 3.130177267
Sum 3087.46
Variance 421.6344541
Monotonicity Not monotonic
2022-07-04T20:24:05.244104 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Value Count Frequency (%)
9.37 2
1.4%
14.18 2
1.4%
16.08 1
0.7%
26.09 1
0.7%
11.38 1
0.7%
125 1
0.7%
12.39 1
0.7%
11.78 1
0.7%
6.56 1
0.7%
21.97 1
0.7%
Other values (124) 124
89.9%
(Missing) 2
1.4%
Value Count Frequency (%)
4.71 1
0.7%
5.37 1
0.7%
6.56 1
0.7%
6.72 1
0.7%
6.89 1
0.7%
7.67 1
0.7%
7.68 1
0.7%
7.72 1
0.7%
7.75 1
0.7%
8.19 1
0.7%
Value Count Frequency (%)
126.49 1
0.7%
125 1
0.7%
116.21 1
0.7%
83.34 1
0.7%
78.73 1
0.7%
63.22 1
0.7%
60.96 1
0.7%
60.19 1
0.7%
52.48 1
0.7%
52.32 1
0.7%

Group time: Chatbot filters
Real number (ℝ ≥0 )

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct 132
Distinct (%) 100.0%
Missing 6
Missing (%) 4.3%
Infinite 0
Infinite (%) 0.0%
Mean 190.9678788
Minimum 25.05
Maximum 8381.27
Zeros 0
Zeros (%) 0.0%
Negative 0
Negative (%) 0.0%
Memory size 1.2 KiB
2022-07-04T20:24:05.550965 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum 25.05
5-th percentile 34.52
Q1 52.7075
median 70.325
Q3 102.89
95-th percentile 298.8625
Maximum 8381.27
Range 8356.22
Interquartile range (IQR) 50.1825

Descriptive statistics

Standard deviation 776.0668391
Coefficient of variation (CV) 4.063860603
Kurtosis 97.12597141
Mean 190.9678788
Median Absolute Deviation (MAD) 21.235
Skewness 9.459546008
Sum 25207.76
Variance 602279.7387
Monotonicity Not monotonic
2022-07-04T20:24:05.834312 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Value Count Frequency (%)
43.86 1
0.7%
110.22 1
0.7%
57.73 1
0.7%
40.56 1
0.7%
115.78 1
0.7%
70.23 1
0.7%
123.09 1
0.7%
80.41 1
0.7%
76.86 1
0.7%
96.04 1
0.7%
Other values (122) 122
88.4%
(Missing) 6
4.3%
Value Count Frequency (%)
25.05 1
0.7%
25.26 1
0.7%
27.9 1
0.7%
29.04 1
0.7%
29.71 1
0.7%
30.43 1
0.7%
34.08 1
0.7%
34.88 1
0.7%
35.7 1
0.7%
36.61 1
0.7%
Value Count Frequency (%)
8381.27 1
0.7%
2708.79 1
0.7%
1816.03 1
0.7%
1188.3 1
0.7%
336.91 1
0.7%
318.36 1
0.7%
301.75 1
0.7%
296.5 1
0.7%
290.14 1
0.7%
270.72 1
0.7%

Group time: User experience
Real number (ℝ ≥0 )

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct 128
Distinct (%) 100.0%
Missing 10
Missing (%) 7.2%
Infinite 0
Infinite (%) 0.0%
Mean 250.5772656
Minimum 10.37
Maximum 10869.3
Zeros 0
Zeros (%) 0.0%
Negative 0
Negative (%) 0.0%
Memory size 1.2 KiB
2022-07-04T20:24:06.128077 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum 10.37
5-th percentile 38.258
Q1 58.31
median 78.025
Q3 110.3175
95-th percentile 268.39
Maximum 10869.3
Range 10858.93
Interquartile range (IQR) 52.0075

Descriptive statistics

Standard deviation 1115.203316
Coefficient of variation (CV) 4.450536698
Kurtosis 71.45108581
Mean 250.5772656
Median Absolute Deviation (MAD) 24.22
Skewness 8.171652707
Sum 32073.89
Variance 1243678.437
Monotonicity Not monotonic
2022-07-04T20:24:06.424928 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Value Count Frequency (%)
48.54 1
0.7%
58.54 1
0.7%
104.3 1
0.7%
121.79 1
0.7%
485.24 1
0.7%
1292.74 1
0.7%
79.15 1
0.7%
92.8 1
0.7%
70.2 1
0.7%
77.61 1
0.7%
Other values (118) 118
85.5%
(Missing) 10
7.2%
Value Count Frequency (%)
10.37 1
0.7%
20.14 1
0.7%
21.35 1
0.7%
26.64 1
0.7%
30.94 1
0.7%
36.77 1
0.7%
37.95 1
0.7%
38.83 1
0.7%
39.22 1
0.7%
40.12 1
0.7%
Value Count Frequency (%)
10869.3 1
0.7%
6157.66 1
0.7%
2598.22 1
0.7%
1292.74 1
0.7%
485.24 1
0.7%
442.85 1
0.7%
290.16 1
0.7%
227.96 1
0.7%
202.14 1
0.7%
185.23 1
0.7%

Group time: Badges
Real number (ℝ ≥0 )

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct 124
Distinct (%) 97.6%
Missing 11
Missing (%) 8.0%
Infinite 0
Infinite (%) 0.0%
Mean 49.9696063
Minimum 4.74
Maximum 612.67
Zeros 0
Zeros (%) 0.0%
Negative 0
Negative (%) 0.0%
Memory size 1.2 KiB
2022-07-04T20:24:06.728427 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum 4.74
5-th percentile 18.1
Q1 28.25
median 36.51
Q3 47.52
95-th percentile 109.589
Maximum 612.67
Range 607.93
Interquartile range (IQR) 19.27

Descriptive statistics

Standard deviation 63.57371316
Coefficient of variation (CV) 1.27224763
Kurtosis 51.14326915
Mean 49.9696063
Median Absolute Deviation (MAD) 9.44
Skewness 6.461007721
Sum 6346.14
Variance 4041.617005
Monotonicity Not monotonic
2022-07-04T20:24:07.008075 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Value Count Frequency (%)
25.61 2
1.4%
43.11 2
1.4%
25.63 2
1.4%
25.42 1
0.7%
43.94 1
0.7%
30.96 1
0.7%
31.15 1
0.7%
29.46 1
0.7%
32.41 1
0.7%
63.61 1
0.7%
Other values (114) 114
82.6%
(Missing) 11
8.0%
Value Count Frequency (%)
4.74 1
0.7%
9.35 1
0.7%
12.02 1
0.7%
12.26 1
0.7%
13.06 1
0.7%
14.28 1
0.7%
17.71 1
0.7%
19.01 1
0.7%
20.33 1
0.7%
21.8 1
0.7%
Value Count Frequency (%)
612.67 1
0.7%
299.84 1
0.7%
245.87 1
0.7%
208.25 1
0.7%
136.09 1
0.7%
116.25 1
0.7%
112.07 1
0.7%
103.8 1
0.7%
100.42 1
0.7%
90.64 1
0.7%

Group time: Messages
Real number (ℝ ≥0 )

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct 127
Distinct (%) 100.0%
Missing 11
Missing (%) 8.0%
Infinite 0
Infinite (%) 0.0%
Mean 82.05669291
Minimum 5.86
Maximum 3296.84
Zeros 0
Zeros (%) 0.0%
Negative 0
Negative (%) 0.0%
Memory size 1.2 KiB
2022-07-04T20:24:07.301630 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum 5.86
5-th percentile 16.321
Q1 26.91
median 35.86
Q3 50.19
95-th percentile 190.275
Maximum 3296.84
Range 3290.98
Interquartile range (IQR) 23.28

Descriptive statistics

Standard deviation 298.7383757
Coefficient of variation (CV) 3.640633873
Kurtosis 108.690655
Mean 82.05669291
Median Absolute Deviation (MAD) 10.88
Skewness 10.12685324
Sum 10421.2
Variance 89244.61714
Monotonicity Not monotonic
2022-07-04T20:24:07.576055 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Value Count Frequency (%)
20.99 1
0.7%
57.31 1
0.7%
35.76 1
0.7%
38.82 1
0.7%
55.13 1
0.7%
276.34 1
0.7%
36.42 1
0.7%
36.67 1
0.7%
28.49 1
0.7%
29.54 1
0.7%
Other values (117) 117
84.8%
(Missing) 11
8.0%
Value Count Frequency (%)
5.86 1
0.7%
10.47 1
0.7%
11.23 1
0.7%
12.46 1
0.7%
13.78 1
0.7%
14.61 1
0.7%
16.15 1
0.7%
16.72 1
0.7%
17.72 1
0.7%
18.43 1
0.7%
Value Count Frequency (%)
3296.84 1
0.7%
655.29 1
0.7%
435.43 1
0.7%
371 1
0.7%
276.34 1
0.7%
267.62 1
0.7%
193.65 1
0.7%
182.4 1
0.7%
142.73 1
0.7%
138.6 1
0.7%

Group time: Social behaviours
Real number (ℝ ≥0 )

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct 126
Distinct (%) 99.2%
Missing 11
Missing (%) 8.0%
Infinite 0
Infinite (%) 0.0%
Mean 50.4303937
Minimum 7.1
Maximum 240.99
Zeros 0
Zeros (%) 0.0%
Negative 0
Negative (%) 0.0%
Memory size 1.2 KiB
2022-07-04T20:24:07.865525 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum 7.1
5-th percentile 21.06
Q1 32.64
median 42.22
Q3 54.43
95-th percentile 112.837
Maximum 240.99
Range 233.89
Interquartile range (IQR) 21.79

Descriptive statistics

Standard deviation 35.50580872
Coefficient of variation (CV) 0.7040557512
Kurtosis 12.44075063
Mean 50.4303937
Median Absolute Deviation (MAD) 11.19
Skewness 3.185309824
Sum 6404.66
Variance 1260.662453
Monotonicity Not monotonic
2022-07-04T20:24:08.168835 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Value Count Frequency (%)
39.63 2
1.4%
44 1
0.7%
42.35 1
0.7%
44.55 1
0.7%
63.03 1
0.7%
240.99 1
0.7%
64.56 1
0.7%
48.69 1
0.7%
33.33 1
0.7%
38.11 1
0.7%
Other values (116) 116
84.1%
(Missing) 11
8.0%
Value Count Frequency (%)
7.1 1
0.7%
7.74 1
0.7%
10.04 1
0.7%
11.45 1
0.7%
16.8 1
0.7%
20.11 1
0.7%
20.46 1
0.7%
22.46 1
0.7%
22.5 1
0.7%
23.54 1
0.7%
Value Count Frequency (%)
240.99 1
0.7%
214.87 1
0.7%
198.71 1
0.7%
183.94 1
0.7%
124.72 1
0.7%
117.17 1
0.7%
114.31 1
0.7%
109.4 1
0.7%
97.82 1
0.7%
83.88 1
0.7%

Group time: Final question
Real number (ℝ ≥0 )

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct 121
Distinct (%) 95.3%
Missing 11
Missing (%) 8.0%
Infinite 0
Infinite (%) 0.0%
Mean 237.3992126
Minimum 2.3
Maximum 23738.8
Zeros 0
Zeros (%) 0.0%
Negative 0
Negative (%) 0.0%
Memory size 1.2 KiB
2022-07-04T20:24:08.467829 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum 2.3
5-th percentile 2.945
Q1 4.615
median 7.78
Q3 51.71
95-th percentile 222.632
Maximum 23738.8
Range 23736.5
Interquartile range (IQR) 47.095

Descriptive statistics

Standard deviation 2104.576398
Coefficient of variation (CV) 8.865136388
Kurtosis 126.355189
Mean 237.3992126
Median Absolute Deviation (MAD) 4.51
Skewness 11.22728715
Sum 30149.7
Variance 4429241.815
Monotonicity Not monotonic
2022-07-04T20:24:08.750134 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Value Count Frequency (%)
5.72 3
2.2%
5.09 2
1.4%
4.87 2
1.4%
4.26 2
1.4%
3.21 2
1.4%
38.71 1
0.7%
6.79 1
0.7%
3.59 1
0.7%
7.07 1
0.7%
190.77 1
0.7%
Other values (111) 111
80.4%
(Missing) 11
8.0%
Value Count Frequency (%)
2.3 1
0.7%
2.48 1
0.7%
2.62 1
0.7%
2.72 1
0.7%
2.79 1
0.7%
2.85 1
0.7%
2.93 1
0.7%
2.98 1
0.7%
3.01 1
0.7%
3.1 1
0.7%
Value Count Frequency (%)
23738.8 1
0.7%
664.72 1
0.7%
592.45 1
0.7%
423.39 1
0.7%
408.11 1
0.7%
353.68 1
0.7%
230.72 1
0.7%
203.76 1
0.7%
197.89 1
0.7%
190.95 1
0.7%

Interactions

2022-07-04T20:23:21.616075 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:23:03.238404 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:23:05.173002 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:23:07.111566 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:23:09.253302 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:23:11.320948 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:23:13.257478 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:23:15.447751 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:23:17.433575 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:23:19.633274 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:23:21.804288 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:23:03.434511 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:23:05.356949 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:23:07.302963 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:23:09.452443 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:23:11.507971 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:23:13.645349 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:23:15.637317 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:23:17.623839 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:23:19.823680 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:23:21.990886 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:23:03.617491 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:23:05.539915 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:23:07.493332 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:23:09.650933 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:23:11.694808 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:23:13.841899 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:23:15.828016 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:23:17.816836 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:23:20.013985 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:23:22.183073 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:23:03.807988 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:23:05.733372 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:23:07.689639 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:23:09.858607 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:23:11.886689 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:23:14.037231 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:23:16.024972 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:23:18.017761 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:23:20.214037 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:23:22.386631 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:23:04.012357 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:23:05.937575 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:23:08.082154 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:23:10.084912 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:23:12.090309 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:23:14.245531 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:23:16.234414 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:23:18.228851 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:23:20.426722 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:23:22.574865 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:23:04.196436 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:23:06.124981 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:23:08.270172 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:23:10.286881 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:23:12.276967 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:23:14.437942 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:23:16.428424 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:23:18.422459 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:23:20.618639 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:23:22.770862 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:23:04.395890 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:23:06.322746 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:23:08.465424 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:23:10.490909 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:23:12.470366 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:23:14.637002 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:23:16.630423 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:23:18.624074 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:23:20.817691 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:23:22.973051 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:23:04.591162 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:23:06.522791 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:23:08.665497 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:23:10.701581 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:23:12.669085 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:23:14.843610 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:23:16.837293 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:23:18.829941 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:23:21.019860 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:23:23.176100 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:23:04.793064 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:23:06.724606 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:23:08.864925 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:23:10.912012 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:23:12.870042 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:23:15.048947 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:23:17.044422 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:23:19.238067 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:23:21.225385 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:23:23.374676 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:23:04.985503 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:23:06.919417 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:23:09.059335 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:23:11.123924 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:23:13.065718 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:23:15.256046 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:23:17.243715 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:23:19.439685 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:23:21.424577 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/

Correlations

2022-07-04T20:24:09.008142 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient ( ρ ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r . It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y , one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-07-04T20:24:09.706057 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient ( r ) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r .

To calculate r for two variables X and Y , one divides the covariance of X and Y by the product of their standard deviations.
2022-07-04T20:24:10.148403 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient ( τ ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y , one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-07-04T20:24:10.718713 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here .
2022-07-04T20:24:12.620197 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here .

Missing values

2022-07-04T20:23:24.322031 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-07-04T20:23:27.590445 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-07-04T20:23:33.319442 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.